Patent Grant
12064258
The disclosure relates to methods and devices for classification of an ostomy condition, and in particular for image-based classification of an ostomy condition.
The accompanying drawings are included to provide a further understanding of embodiments and are incorporated into and a part of this specification. The drawings illustrate embodiments and together with the description serve to explain principles of embodiments. Other embodiments and many of the intended advantages of embodiments will be readily appreciated as they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale relative to each other. Like reference numerals designate corresponding similar parts.
Various exemplary embodiments and details are described hereinafter, with reference to the figures when relevant. It should be noted that the figures may or may not be drawn to scale and that elements of similar structures or functions are represented by like reference numerals throughout the figures. It should also be noted that the figures are only intended to facilitate the description of the embodiments. They are not intended as an exhaustive description of the invention or as a limitation on the scope of the invention. In addition, an illustrated embodiment needs not have all the aspects or advantages shown. An aspect or an advantage described in conjunction with a particular embodiment is not necessarily limited to that embodiment and can be practiced in any other embodiments even if not so illustrated, or if not so explicitly described.
Throughout the present disclosure, the words “stoma” and “ostomy” are used to denote a surgically created opening bypassing the intestines or urinary tract system of a person. The words are used interchangeably, and no differentiated meaning is intended. The same applies for any words or phrases derived from these, e.g. “stomal”, “ostomies” etc. Also, the solid and liquid wastes emanating from the stoma may be referred to as both stomal “output,” “waste(s),” and “fluids” interchangeably. A subject having undergone ostomy surgery may be referred to as “ostomist” or “ostomate”—moreover, also as “patient” or “user”. However, in some cases “user” may also relate or refer to a health care professional (HCP), such as a surgeon or an ostomy care nurse or others. In those cases, it will either be explicitly stated, or be implicit from the context that the “user” is not the “patient” him- or herself.
Throughout the present disclosure, the term “stomal area” denotes the stoma and an area around the stoma (peristomal area). The “peristomal area” denotes the area around the stoma covered by the adhesive surface when the ostomy appliance is attached to the skin of the user in its intended position during use.
In the following, whenever referring to proximal side or surface of a layer, an element, a device or part of a device, the referral is to the skin-facing side or surface, when a user wears the ostomy appliance. Likewise, whenever referring to the distal side or surface of a layer, an element, a device or part of a device, the referral is to the side or surface facing away from the skin, when a user wears the ostomy appliance. In other words, the proximal side or surface is the side or surface closest to the user, when the appliance is fitted on a user and the distal side is the opposite side or surface—the side or surface furthest away from the user in use.
The axial direction is defined as the direction of the stoma, when a user wears the appliance. Thus, the axial direction is generally perpendicular to the skin or abdominal surface of the user.
A radial direction is defined as perpendicular to the axial direction. In some sentences, the words “inner” and “outer” may be used. These qualifiers should generally be perceived with respect to the radial direction, such that a reference to an “outer” element means that the element is farther away from a centre portion of the ostomy appliance than an element referenced as “inner”. In addition, “innermost” should be interpreted as the portion of a component forming a centre of the component and/or being adjacent to the centre of the component. In analogy, “outermost” should be interpreted as a portion of a component forming an outer edge or outer contour of a component and/or being adjacent to that outer edge or outer contour.
The use of the word “substantially” as a qualifier to certain features or effects in this disclosure is intended to simply mean that any deviations are within tolerances that would normally be expected by the skilled person in the relevant field.
The use of the word “generally” as a qualifier to certain features or effects in this disclosure is intended to simply mean—for a structural feature: that a majority or major portion of such feature exhibits the characteristic in question, and—for a functional feature or an effect: that a majority of outcomes involving the characteristic provide the effect, but that exceptionally outcomes do no provide the effect.
The present disclosure relates to methods, devices, ostomy system, and devices thereof and in particular methods and devices for classifying an ostomy condition. The ostomy system comprises one or more of an ostomy appliance and one or more accessory devices. An accessory device (also referred to as an external device) may be a mobile phone (e.g. a smartphone), tablet computer, or other handheld device. An accessory device may be a personal electronic device, e.g. a wearable, such as a watch or other wrist-worn electronic device. The ostomy system may comprise a server device. The server device may be operated and/or controlled by the ostomy appliance manufacturer and/or a service centre.
A method for classifying an ostomy condition is provided, the method comprising obtaining image data, e.g. with an accessory device, the image data optionally comprising stoma image data of a stomal area including a stoma and/or appliance image data of an adhesive surface of an ostomy appliance; determining one or more ostomy representations, optionally including a first ostomy representation and/or a first ostomy parameter, based on the image data, such as based on the stoma image data and/or the appliance image data, and/or based on transformed image data; and outputting a first ostomy representation and/or the first ostomy parameter. The method optionally comprises transforming the image data. Determining the one or more ostomy representations based on the image data optionally comprises determining the first ostomy representation and/or the first ostomy parameter based on the image data and/or the transformed image data.
It is an advantage of the present disclosure that improved classification of ostomy condition is provided by compensating for image data of poor quality, e.g. image data obtained from different positions. Further, an improved classification of ostomy condition is provided by securing a uniform handling and/or interpretation of image data.
Also, it is an important advantage of the present disclosure that a more accurate classification of ostomy condition is provided by determining ostomy parameters in a uniform way. Further, the present disclosure allows for improved resolution in classifying an ostomy condition, e.g. resulting in classifying an ostomy condition into a larger number of ostomy condition types.
One or more exemplary methods for classifying an ostomy condition comprises:
One or more exemplary methods for classifying an ostomy condition comprises:
The method comprises obtaining image data, such as stoma image data and/or appliance image data. In one or more exemplary methods, obtaining image data may comprise capturing image data with a camera and transmitting the image data to a server device. In one or more exemplary methods, obtaining image data may comprise receiving, at a server device, the image data.
The method optionally comprises transforming the image data. In one or more exemplary methods, transforming the image data comprises transforming the image data with server device or with accessory device. Transforming the image data may comprise transmitting the transformed image data or parts thereof to server device. Transforming the image data may comprise receiving, in server device, the transformed image data or parts thereof.
The method comprises determining one or more ostomy representations, e.g. with accessory device and/or server device. Determining one or more ostomy representations may comprise receiving, with the accessory device, the one or more ostomy representations from server device.
In one or more exemplary methods, determining one or more ostomy representations including a first ostomy parameter based on the image data comprises:
In one or more exemplary methods, determining one or more ostomy representations including a first ostomy parameter based on the image data comprises:
An image representation may be a binary mask. Accordingly, the one or more image representations may be a binary mask. In other words, determining one or more ostomy representations may comprise determining one or more binary masks based on the image data or the transformed image data. In one or more exemplary methods, determining one or more image representations based on the image data are performed by convolutional neural network with N layers, e.g. in the range from 10-50 layers.
The method may comprise storing the one or more image representations, optionally including a stoma identifier and/or a user identifier.
In one or more exemplary methods and/or devices, the one or more image representations comprises a stoma background image representation, e.g. based on stoma image data and/or transformed stoma image data. The stoma background image representation is indicative of a background of the stoma image data, i.e. which part(s)/pixels of the stoma image data that are regarded or identified as background (e.g. including part of user skin not covered by adhesive surface of ostomy appliance). Determining one or more ostomy representations, such as the first ostomy representation and/or a third ostomy representation, may be based on the stoma background image representation. The stoma background image representation may have a resolution of 256×256 pixels or more, such as 512×512 pixels.
In one or more exemplary methods and/or devices, the one or more image representations comprises an appliance background image representation, e.g. based on appliance image data and/or transformed appliance image data. The appliance background image representation is indicative of a background of the appliance image data, i.e. which part(s)/pixels of the appliance image data that are regarded or identified as background (e.g. image part(s)/pixel(s) outside the area of the adhesive surface of the ostomy appliance). Determining one or more ostomy representations, such as the second ostomy representation and/or a third ostomy representation, may be based on the appliance background image representation.
In one or more exemplary methods and/or devices, the one or more image representations comprises a stoma image representation, e.g. based on stoma image data and/or transformed stoma image data. The stoma image representation is indicative of the stoma, i.e. which part(s)/pixels of the ostomy image data that are regarded or identified as the stoma. Determining one or more ostomy representations, such as the first ostomy representation and/or a third ostomy representation, may be based on the stoma image representation. The stoma image representation may have a resolution of 256×256 pixels or more, such as 512×512 pixels.
In one or more exemplary methods and/or devices, the one or more image representations comprises a normal skin image representation, e.g. based on stoma image data and/or transformed stoma image data. The normal skin image representation is indicative of the normal skin of the peristomal area, i.e. which part(s)/pixels of the ostomy image data that are regarded or identified as not having discoloration. Determining one or more ostomy representations, such as the first ostomy representation and/or a third ostomy representation, may be based on the normal skin image representation. The normal skin image representation may have a resolution of 256×256 pixels or more, such as 512×512 pixels.
In one or more exemplary methods and/or devices, the one or more image representations comprises one or more, such as two, three, four or more, stoma discoloration representations. A stoma discoloration representation may be indicative of a discoloration of the peristomal area, i.e. which part(s)/pixels of the ostomy image data that are regarded or identified as the peristomal area and discoloured. A stoma discoloration representation may have a resolution of 256×256 pixels or more, such as 512×512 pixels.
In one or more exemplary methods and/or devices, the one or more image representations comprises a first stoma discoloration representation, e.g. based on stoma image data and/or transformed stoma image data. The first stoma discoloration representation may be indicative of a first discoloration of the peristomal area, i.e. which part(s)/pixels of the ostomy image data that are regarded or identified as the peristomal area and have a first discoloration (e.g. first degree of redness). The first stoma discoloration representation may be indicative of part(s)/pixels of the ostomy image data within the peristomal area having a colour parameter, such as the red channel of an RGB image, within a first range or less than a first threshold, such as less than 0.25. The first stoma discoloration representation may be indicative of part(s)/pixels of the peristomal area with little or no discoloration. Determining one or more ostomy representations may be based on the first stoma discoloration representation.
In one or more exemplary methods and/or devices, the method comprises determining the first stoma discoloration representation based on red channel data of the image data/stoma image data.
In one or more exemplary methods and/or devices, the one or more image representations comprises a second stoma discoloration representation, e.g. based on stoma image data and/or transformed stoma image data. The second stoma discoloration representation may be indicative of a second discoloration of the peristomal area, i.e. which part(s)/pixels of the ostomy image data that are regarded or identified as the peristomal area and have a second discoloration (e.g. second degree of redness). The second discoloration is different from the first discoloration. The second stoma discoloration representation may be indicative of part(s)/pixels of the ostomy image data within the peristomal area having a colour parameter, such as the red channel of an RGB image, within a second range, e.g. in the range from 0.25 to 0.5. The second stoma discoloration representation may be indicative of part(s)/pixels of the peristomal area with small, medium or high discoloration depending on the values of the second range. Determining one or more ostomy representations may be based on the second stoma discoloration representation.
In one or more exemplary methods and/or devices, the method comprises determining the second stoma discoloration representation based on red channel data of the image data/stoma image data.
In one or more exemplary methods and/or devices, the one or more image representations comprises a third stoma discoloration representation, e.g. based on stoma image data and/or transformed stoma image data. The third stoma discoloration representation may be indicative of a third discoloration of the peristomal area, i.e. which part(s)/pixels of the ostomy image data that are regarded or identified as the peristomal area and have a third discoloration (e.g. third degree of redness). The third stoma discoloration representation may be indicative of part(s)/pixels of the ostomy image data within the peristomal area having a colour parameter, such as the red channel of an RGB image, within a third range, e.g. in the range from 0.5 to 0.75. The third stoma discoloration representation may be indicative of part(s)/pixels of the peristomal area with medium or high discoloration. Determining one or more ostomy representations may be based on the third stoma discoloration representation.
In one or more exemplary methods and/or devices, the method comprises determining the third stoma discoloration representation based on red channel data of the image data/stoma image data.
In one or more exemplary methods and/or devices, the one or more image representations comprises a fourth stoma discoloration representation, e.g. based on stoma image data and/or transformed stoma image data. The fourth stoma discoloration representation may be indicative of a fourth discoloration of the peristomal area, i.e. which part(s)/pixels of the ostomy image data that are regarded or identified as the peristomal area and have a fourth discoloration (e.g. fourth degree of redness). The fourth stoma discoloration representation may be indicative of part(s)/pixels of the ostomy image data within the peristomal area having a colour parameter, such as the red channel of an RGB image, within a fourth range, e.g. in the range from 0.75 to 1, or larger than a fourth threshold. The fourth stoma discoloration representation may be indicative of part(s)/pixels of the peristomal area with high discoloration. Determining one or more ostomy representations may be based on the fourth stoma discoloration representation.
In one or more exemplary methods, the method comprises determining the fourth stoma discoloration representation based on red channel data of the image data/stoma image data.
In one or more exemplary methods, determining one or more image representations based on the image data comprises determining a base colour parameter, e.g. including a first base colour parameter and/or a second base colour parameter, and determining the one or more image representations and/or one or more ostomy parameters based on the base colour parameter. The base colour parameter may be based on red channel data of the ostomy image data.
In one or more exemplary methods, determining one or more image representations and/or transforming image data optionally comprises applying an image conversion to the image data, such as the stoma image data. The image conversion may be based on one or more colour channels including the red channel R and optionally the blue channel and/or the green channel of the image being converted. The converted image I_C may be given as:
I_C=Abs(R−Average(G−B),
where R is the red channel in the image, G is the green channel and B is the blue channel. In other words, the red, blue, and green channels of the image may be converted into a single combined channel also denoted CC for each pixel of the image. The converted image I_C may be a linear combination of the red, blue, and green channels.
In one or more exemplary methods, determining second ostomy parameter(s) may be based on the first base colour parameter and/or the second base colour parameter.
In one or more exemplary methods, a first base colour parameter is indicative of a lower discoloration limit (i.e. corresponding to a discoloration of 0%) and optionally corresponding to a minimum of discoloration of pixels in the fourth stoma image representation (a first discoloration representation indicative of a discoloration of the peristomal area). The first base colour parameter may correspond to an R or CC pixel value of 0 or the lowest R or CC pixel value in the (converted) image. The first base colour parameter may be based on the CC value of pixels in the stoma image data identified as normal skin, e.g. for pixels near and outside a first boundary line indicative of a circumference or edge of the stomal area. Thus, the colour of skin not being covered by adhesive may be used as a reference or baseline for no discoloration.
In one or more exemplary methods, a second base colour parameter is indicative of an upper discoloration limit (i.e. corresponding to a pixel discoloration of 100%) and optionally corresponding to a maximum red channel pixel value or a maximum combined channel pixel value in the second stoma image representation (stoma image representation indicative of the stoma). Thus, the colour of the stoma (which is always red) may be used as a reference colour, in turn providing more uniform results and accommodating differences in light conditions when obtaining image data.
In one or more exemplary methods and/or devices, the one or more image representations comprises one or more, such as two, three, four or more, appliance discoloration representations. An appliance discoloration representation may be indicative of a discoloration of the adhesive surface of the ostomy appliance, i.e. which part(s)/pixels of the appliance image data that are regarded or identified as the adhesive surface and discoloured. An appliance discoloration representation may have a resolution of 256×256 pixels or more, such as 512×512 pixels.
The one or more image representations may comprise a first appliance discoloration representation, e.g. based on appliance image data and/or transformed appliance image data. The first appliance discoloration representation may be indicative of a first discoloration of the adhesive surface of the ostomy appliance, i.e. which part(s)/pixels of the appliance image data that are regarded or identified as the adhesive surface and have a first discoloration (e.g. first degree of output or simply output). The first appliance discoloration representation may be indicative of part(s)/pixels of the appliance image data having a colour parameter, such as the red channel and/or the green channel of an RGB image, within a first range or less than a first threshold, such as less than 0.25. The first appliance discoloration representation may be indicative of part(s)/pixels of the adhesive surface with little or medium discoloration. Determining one or more ostomy representations may be based on the first appliance discoloration representation.
The one or more image representations may comprise a second appliance discoloration representation, e.g. based on appliance image data and/or transformed appliance image data. The second appliance discoloration representation may be indicative of a second discoloration of the adhesive surface of the ostomy appliance, i.e. which part(s)/pixels of the appliance image data that are regarded or identified as the adhesive surface and have a second discoloration (e.g. second degree of output). The second appliance discoloration representation may be indicative of part(s)/pixels of the appliance image data having a colour parameter, such as the red channel and/or the green channel of an RGB image, within a second range or larger than a second threshold. The second appliance discoloration representation may be indicative of part(s)/pixels of the adhesive surface with medium or high discoloration. Determining one or more ostomy representations may be based on the second appliance discoloration representation.
In one or more exemplary methods and/or devices, the one or more image representations comprises a stomal opening image representation, e.g. based on appliance image data and/or transformed appliance image data. The stomal opening image representation is indicative of the stomal opening, i.e. which part(s)/pixels of the appliance image data that are regarded or identified as the stomal opening. Determining one or more ostomy representations may be based on the stomal opening image representation. The stomal opening image representation may have a resolution of 256×256 pixels or more, such as 512×512 pixels.
The one or more image representations may comprise an appliance area representation, e.g. based on appliance image data and/or transformed appliance image data. The appliance area representation may be indicative of no appliance discoloration on the adhesive surface, i.e. no leak of output and thus which part(s)/pixels of the appliance image data that are regarded or identified as the adhesive surface and not being discoloured by output. Determining one or more ostomy representations may be based on the appliance area representation.
In one or more exemplary methods, determining one or more ostomy representations comprises determining an ostomy representation by combining a plurality of image representations. Determining one or more ostomy representations may comprise overlaying one or more image representations, such as one or more stoma discoloration representations, on the stoma image data or on the transformed stoma image data. Determining one or more ostomy representations may comprise overlaying one or more image representations, such as one or more appliance discoloration representations, on the appliance image data or on the transformed appliance image data.
Determining one or more ostomy representations, such as the first ostomy representation may comprise determining a discoloration map based on the stoma image data or on the transformed stoma image data and overlaying the discoloration map on the stoma image data or on the transformed stoma image data. In other words, the first ostomy representation may comprise a discoloration map.
Determining a discoloration map based on the stoma image data or on the transformed stoma image data may comprise assigning a first colour value to pixels of the peristomal area that are discoloured to a first degree in a first range. Determining a discoloration map based on the stoma image data or on the transformed stoma image data may comprise assigning a second colour value to pixels of the peristomal area that are discoloured to a second degree in a second range and/or assigning a third colour value to pixels of the peristomal area that are discoloured to a third degree in a third range. Four, five, six, seven, nine, ten, or more different colour values may be assigned to four, five, six, seven, nine, ten, or more different ranges. Thus, the discoloration map may comprise first pixels having a first colour value, second pixels having a second colour value and optionally third pixels having a third colour value.
In one or more exemplary methods, determining one or more ostomy representations comprises determining a first ostomy representation and/or a second ostomy representation by combining a plurality of image representations.
The method comprises outputting one or more ostomy representations, e.g. the first ostomy representation and/or the second ostomy representation, and/or outputting the first ostomy parameter. Outputting first ostomy representation and/or first ostomy parameter may comprise displaying the first ostomy parameter or a first ostomy representation comprising the first ostomy parameter on a display of an accessory device. Outputting first ostomy representation and/or first ostomy parameter may comprise receiving, in the accessory device, the first ostomy representation and/or the first ostomy parameter. Outputting first ostomy representation and/or first ostomy parameter may comprise transmitting, with server device, the first ostomy representation and/or the first ostomy parameter, e.g. to the accessory device. Outputting ostomy representation(s) may comprise storing the ostomy representations in memory of the accessory device and/or server device.
Determining the one or more ostomy representations based on the image data may comprise determining a first ostomy representation, OR_1, based on the image data, ID, and/or transformed image data, ID_T, e.g. the stoma image data and/or the appliance image data. The first ostomy representation OR_1, also optionally denoted stoma representation, may be indicative of discoloration of the stomal area of the user. The first ostomy representation OR_1 may comprise or be overlaid on the stoma image data or transformed stoma image data. The first ostomy representation may comprise the first ostomy parameter, OP_1, and/or second ostomy parameter(s), P_2_1, P_2_2, . . . . The first ostomy representation may comprise stoma image data, SID, and/or transformed stoma image data, SID_T.
The method comprises determining one or more ostomy representations including a first ostomy parameter based on the one or more image representations.
The first ostomy parameter may be a discoloration index indicative of discoloration of the stomal area. The second ostomy parameter, also denoted OP_2, or second set of second ostomy parameters may be indicative of discoloration of the stomal area, such as indicative of discoloration severity percentage or degrees of discoloration of the peristomal area. The second set of ostomy parameters optionally comprises two or more second parameters, such as three, four, five, six, seven, eight, nine, ten or more second parameters. The second set of ostomy parameters optionally comprises a second primary ostomy parameter, also denoted OP_2_1, and a second secondary ostomy parameter, also denoted OP_2_2. The second set of ostomy parameters optionally comprises a second tertiary ostomy parameter, also denoted OP_2_3, and/or a second quaternary ostomy parameter, also denoted OP_2_4.
The first ostomy parameter may be indicative of how much of the peristomal area that is discoloured. For example, the first ostomy parameter OP_1 may be based on the one or more stoma discoloration representations and may be given by
OP_1=N_TOT/N_PA,
Where N_TOT is the total number of discoloured pixels in the peristomal area and N_PA is the total number of pixels in the peristomal area.
The first ostomy parameter may be indicative of an area of discoloured peristomal area.
For example, the first ostomy parameter OP_1 may be based on the one or more stoma discoloration representations and may be given by
OP_1=APP*N_TOT
Where N_TOT is the total number of discoloured pixels in the peristomal area and APP is an area per pixel.
The area of a pixel APP may be given as:
AAP=HPP*WPP,
wherein HPP is the height per pixel and WPP is the width per pixel.
The height per pixel HPP may be based on one or more of the image representations, such as a stoma background image representation and/or an appliance background image representation.
The height per pixel, HPP may be given as:
HPP=AH/PH,
wherein AH is a height of the appliance (e.g. retrieved from a database) and PH is a pixel height of the appliance optionally determined as a number of pixels between two edges of the appliance counted along a vertical axis, e.g. in the appliance background image representation.
The width per pixel, WPP may be given as
WPP=AW/PW,
wherein AW is a width of the appliance (e.g. retrieved from a database) and PW is a pixel width of the appliance optionally determined as the number of pixels between two edges of the appliance counted along a horizontal axis, e.g. in the appliance background image representation.
The method optionally comprises determining one or more second ostomy parameters based on the one or more image representations. The method optionally comprises outputting the one or more third second parameters.
A second primary ostomy parameter may be indicative of how many of the discoloured pixels in the peristomal area that are discoloured to a first degree or indicative of the area of pixels in the peristomal area that are discoloured to a first degree. For example, the second primary ostomy parameter OP_2_1 may be based on the one or more stoma discoloration representations and may be given by
OP_2_1=N_DIS_1/N_TOT,
where N_DIS_1 is the number of discoloured pixels in the peristomal area that are discoloured to a first degree, e.g. less than 0.25, or to a first degree within a first range, where a discoloration of 0% corresponds to a first base colour parameter indicative of a lower discoloration limit of the stoma and a discoloration of 100% corresponds to a second base colour parameter indicative of a maximum red channel pixel value or a maximum combined channel pixel value in the second stoma image representation (stoma image representation indicative of the stoma). N_TOT is the total number of discoloured pixels in the peristomal area. In other words, the red channel pixel intensity of each pixel in the peristomal area is evaluated and compared to a discoloration scale where 0% corresponds to a minimum of discoloration of pixels in the fourth stoma image representation (a first discoloration representation indicative of a discoloration of the peristomal area) and where 100% corresponds to a maximum red channel pixel value in the second stoma image representation (stoma image representation indicative of the stoma). Put in another way, each pixel of the fourth stoma image representation is evaluated to assign a discoloration degree (selected from at least a first degree and a second degree) to each pixel of the in a plurality of discoloration degrees.
One or more second primary ostomy parameters may be indicative of the area, degree, or number of discoloured pixels within a first region of the peristomal area, such as within one or more first radial distances, e.g. 1 cm, 2 cm, and 3 cm, from the edge of the stoma. In other words, the first region may be seen as an inner area of the peristomal area.
One or more second secondary ostomy parameters may be indicative of the area, degree, or number of discoloured pixels within a second region of the peristomal area, such as outside one or more first radial distances, such as 1 cm, 2 cm, and 3 cm, from the edge of the stoma. In other words, the second region may be seen as an outer area of the peristomal area.
A second secondary ostomy parameter may be indicative of how many of the discoloured pixels in the peristomal area that are discoloured to a second degree or indicative of the area of pixels in the peristomal area that are discoloured to a second degree. For example, the second secondary ostomy parameter OP_2_2 may be based on the one or more stoma discoloration representations and may be given by
OP_2_2=N_DIS_2/N_TOT,
where N_DIS_2 is the number of discoloured pixels in the peristomal area that are discoloured to a second degree, e.g. in a second range such as between 0.25 and 0.5, and N_TOT is the total number of discoloured pixels in the peristomal area.
A second tertiary ostomy parameter may be indicative of how many of the discoloured pixels in the peristomal area that are discoloured to a third degree or indicative of the area of pixels in the peristomal area that are discoloured to a third degree. For example, the second tertiary ostomy parameter OP_2_3 may be based on the one or more stoma discoloration representations and may be given by
OP_2_3=N_DIS_3/N_TOT,
where N_DIS_3 is the number of discoloured pixels in the peristomal area that are discoloured to a third degree, e.g. in a third range such as between 0.5 and 0.75, and N_TOT is the total number of discoloured pixels in the peristomal area.
A second quaternary ostomy parameter may be indicative of how many of the discoloured pixels in the peristomal area that are discoloured to a fourth degree or indicative of the area of pixels in the peristomal area that are discoloured to a fourth degree. For example, the second quaternary ostomy parameter OP_2_4 may be based on the one or more stoma discoloration representations and may be given by
OP_2_4=N_DIS_4/N_TOT,
where N_DIS_4 is the number of discoloured pixels in the peristomal area that are discoloured to a fourth degree, e.g. in a fourth range such as between 0.75 and 1 or larger than a threshold, and N_TOT is the total number of discoloured pixels in the peristomal area.
The first ostomy parameter may be a leakage parameter indicative of output distribution on the adhesive surface of the ostomy appliance. The second ostomy parameter or second set of second ostomy parameters may be leakage parameter(s) indicative of output distribution on the adhesive surface of the ostomy appliance.
The first ostomy parameter may be indicative of how much of the adhesive surface of the ostomy appliance that is discoloured. For example, the first ostomy parameter OP_1 may be based on the one or more appliance discoloration representations and may be given by
OP_1=N_TOT/N_AA,
Where N_TOT is the total number of discoloured pixels in the adhesive surface area and N_AA is the total number of pixels in the adhesive surface area.
The first ostomy parameter may be indicative of a discoloured area of the adhesive surface of the ostomy appliance.
For example, the first ostomy parameter OP_1 may be based on the one or more appliance discoloration representations and may be given by
OP_1=APP*N_TOT
Where N_TOT is the total number of discoloured pixels in the adhesive surface of the ostomy appliance and APP is an area per pixel.
The area of a pixel APP may be given as:
AAP=HPP*WPP,
wherein HPP is the height of a pixel and WPP is the width of a pixel.
The height of a pixel HPP may be based on one or more of the image representations, such as a stoma background image representation and/or an appliance background image representation.
The height of a pixel, HPP may be given as:
HPP=AH/PH,
wherein AH is a height of the appliance (e.g. retrieved from a database) and PH is a pixel height of the appliance optionally determined as a number of pixels between two edges of the appliance counted along a vertical axis, e.g. in the appliance background image representation.
The width per pixel, WPP may be given as
WPP=AW/PW,
wherein AW is a width of the appliance (e.g. retrieved from a database) and PW is a pixel width of the appliance optionally determined as the number of pixels between two edges of the appliance counted along a horizontal axis, e.g. in the appliance background image representation.
The method may comprise determining one or more boundary lines based on the one or more image representations. The method may comprise determining a first boundary line, based on the one or more image representations, and wherein an ostomy representation comprises or is based on the first boundary line.
In one or more exemplary methods, the first boundary line may be indicative of a circumference or edge of a stomal area. The first boundary line may be indicative of a circumference or outer edge of an adhesive surface of the ostomy appliance.
The method may comprise determining a second boundary line, based on the one or more image representations, and wherein an ostomy representation comprises or is based on the second boundary line.
In one or more exemplary methods, the second boundary line may be indicative of a circumference or edge of the stoma. The second boundary line may be indicative of a circumference or inner edge of an adhesive surface of the ostomy appliance.
The method may comprise determining a third boundary line, based on the one or more image representations, and wherein an ostomy representation comprises or is based on the third boundary line.
In one or more exemplary methods, the third boundary line may be indicative of a boundary between a normal skin area of the peristomal area (non-discoloured) and a discoloured area of the peristomal area. The third boundary line may be indicative of a circumference or inner edge of an adhesive surface of the ostomy appliance.
The method may comprise determining a fourth boundary line e.g. based on the one or more image representations. Ostomy representation(s) and/or ostomy parameter(s), such as first and/or second ostomy parameter(s), may be based on the fourth boundary line. In one or more exemplary methods, the fourth boundary line may be indicative of a circumference of an output leakage on the adhesive surface of an ostomy appliance.
The first ostomy representation may comprise one or more boundary lines, such as the first boundary line and/or the second boundary line. The first ostomy representation may comprise the first ostomy parameter and/or one or more of the second ostomy parameters. The first ostomy representation may comprise the fourth boundary line.
Determining the one or more ostomy representations based on the image data may comprise determining a second ostomy representation based on the image data and/or transformed image data, e.g. the stoma image data and/or the appliance image data. The second ostomy representation, also denoted appliance representation, may be indicative of output distribution on the adhesive surface of the ostomy appliance. The second ostomy representation may comprise or be overlaid on the appliance image data or transformed appliance image data. The second ostomy representation may comprise one or more boundary lines, such as the first boundary line and/or the second boundary line. The second ostomy representation may comprise the third boundary line and/or the fourth boundary line.
Accordingly, determining one or more ostomy representations based on the image data may comprise determining boundary lines, e.g. of a first ostomy representation and/or of a second ostomy representation.
Determining one or more ostomy representations based on the image data may comprise determining a second ostomy parameter or a set of second ostomy parameters based on the image data and/or transformed image data, such as based on the stoma image data and/or the appliance image data.
The method optionally comprises determining one or more third ostomy parameters based on the one or more image representations. The method optionally comprises outputting the one or more third ostomy parameters.
A third primary ostomy parameter may be indicative of a shortest distance of a leakage of output to an edge of the ostomy appliance. The third ostomy parameter may be based on a first boundary line indicative of a circumference or outer edge of an adhesive surface of the ostomy appliance and a fourth boundary line indicative of a circumference of an output leakage on the adhesive surface of an ostomy appliance. The third ostomy parameter may be determined as a shortest (radial) distance between the first boundary line and the fourth boundary line. An angle may be associated with the third ostomy parameter, e.g. to indicate the direction in which the third ostomy parameter was measured or identified. The angle may be used for determining conversion parameter(s) for conversion between a pixel length and an absolute length.
In one or more exemplary methods, transforming the image data comprises determining a position parameter representative of a position of a camera image plane in relation to the stomal area and/or the adhesive surface, and wherein the transformed image data are based on the position parameter.
In one or more exemplary methods, the position parameter comprises an angle parameter representative of an angle between an optical axis of a camera being the source of the image data and an axial direction of the stomal area/normal to the adhesive surface. The transformed image data may be based on the angle parameter. In other words, transforming the image data may comprise determining an angle parameter representative of an angle between an optical axis of a camera being the source of the image data and an axial direction of the stomal area/normal to the adhesive surface. Thereby is enabled to compensate for image data that are not taken with the optical axis perpendicular to the adhesive surface of the ostomy appliance or perpendicular to the skin surface of the ostomist. Determining an angle parameter may comprise fitting the image data to a stomal area model image and/or an appliance model image and determining the angle parameter based on an image transformation providing a satisfactory fit of the image data to the stomal area model image and/or an appliance model.
In one or more exemplary methods, the position parameter comprises a distance parameter representative of a distance between a camera being the source of the image data and the stomal area/adhesive surface. The transformed image data may be based on the distance parameter. In other words, transforming the image data may comprise determining a distance parameter representative of a distance between a camera being the source of the image data and the stomal area/adhesive surface. A distance parameter allows for a (more precise) determination of a size of the stomal area/adhesive surface. Determining a distance parameter may comprise fitting the image data to a stomal area model image and/or an appliance model image and determining the distance parameter based on an image transformation providing a satisfactory fit of the image data to the stomal area model image and/or an appliance model.
In one or more exemplary methods, the position parameter comprises a rotation parameter representative of a rotational angle between an image axis of the image data and a reference axis of the stomal area/adhesive surface. The transformed image data may be based on the rotation parameter. The reference axis may be a vertical reference axis or a horizontal axis. In other words, transforming the image data may comprise determining a rotation parameter representative of a rotational angle between an image axis of the image data and a reference axis of the stomal area/adhesive surface. A rotation parameter allows to compensate for image data that are rotated, e.g. for a (more precise) determination of a directional ostomy condition. Determining a rotation parameter may comprise fitting the image data to a stomal area model image and/or an appliance model image and determining the rotation parameter based on an image transformation providing a satisfactory fit of the image data to the stomal area model image and/or an appliance model
In one or more exemplary methods, transforming the image data comprises applying a geometric transformation to the image data. The geometric transformation may be based on the position parameter, e.g. one or more of angle parameter, distance parameter, and rotation parameter.
In one or more exemplary methods, transforming the image data comprises fitting the image data to a stomal area model image and/or an appliance model image. For example, transforming the image data may comprise fitting stoma image data to a stomal area model and/or fitting appliance image data to an appliance model image.
In one or more exemplary methods, transforming the image data comprises identifying a first stoma reference indicator on the stomal area. The transformed image data may be based on the first stoma reference indicator. The first stoma reference indicator may be a perimeter of the stoma or other parameters relating to the stoma, e.g. a center of the stoma. Transforming the image data may comprise identifying a second stoma reference indicator and/or a third stoma reference indicator on the stomal area. The transformed image data may be based on the second stoma reference indicator and/or the third stoma reference indicator, e.g. on and/or outside the stomal area. The second stoma reference indicator may be a scar or other body mark, such as a birthmark, belly button, etc. The third stoma reference indicator may be a scar or other body mark, such as a birthmark, belly button, etc. A stoma reference indicator may be indicative of a position and/or a direction of the stoma reference indicator.
In one or more exemplary methods, transforming the image data comprises identifying a first appliance reference indicator on the adhesive surface of the ostomy appliance. The transformed image data may be based on the first appliance reference indicator. The first appliance reference indicator may be a perimeter of the ostomy appliance (or a part thereof), a center of the stomal opening of the ostomy appliance, or a perimeter of the stomal opening of the ostomy appliance. An appliance reference indicator may be indicative of a position and/or a direction of the appliance reference indicator.
In one or more exemplary methods, transforming the image data comprises identifying a second appliance reference indicator on the adhesive surface of the ostomy appliance. The second appliance reference indicator may be different from the first appliance reference indicator and is optionally a perimeter of the ostomy appliance (or a part thereof), a center of the stomal opening of the ostomy appliance, or a perimeter or edge of the stomal opening of the ostomy appliance. The transformed image data may be based on the second appliance reference indicator.
In one or more exemplary methods, transforming the image data comprises scaling, such as downscaling, the image data to a predetermined pixel size, such as N×M pixels, where N may be in the range from 100 to 2,500, e.g. in the range from 100 to 1,000, such as 256 or 512, and where M may be in the range from 100 to 2,500, e.g. in the range from 100 to 1,000, such as 256 or 512. M may be different from N.
In one or more exemplary methods, transforming the image data comprises centering the image data about a center or center region of the image data. In one or more exemplary methods, transforming the image data comprises identifying and selecting a stoma region (stoma region data) of the image data and/or an appliance region (appliance region data) of the image data, and optionally transforming the stoma region and/or the appliance region for provision of respective transformed stoma image data and/or transformed appliance image data. Selecting a stoma region and/or an appliance region may comprise cutting out the stoma region and/or the appliance region from respective stoma image data and/or appliance image data. The stoma region comprises the stomal area, i.e. the stoma and the peristomal area.
In one or more exemplary methods, transforming the image data comprises identifying and selecting a stoma region, centering the stoma region, optionally rotating the stoma region (e.g. using a geometric transformation based on rotation parameter), and downscaling the rotated stoma region for provision of transformed stoma image data.
In one or more exemplary methods, transforming the image data comprises identifying and selecting an appliance region, centering the appliance region, optionally rotating the appliance region (e.g. using a geometric transformation based on rotation parameter), and downscaling the rotated appliance region for provision of transformed appliance image data.
In one or more exemplary methods, scaling the image data comprises determining a scaling parameter. The transformed image data may be based on the scaling parameter. In one or more exemplary methods, determining one or more ostomy representations may comprise upscaling a representation, such as a first representation and/or a second representation or parts thereof, based on the scaling parameter.
In one or more exemplary methods, transforming the image data comprises applying an image conversion to the image data, such as the stoma image data. The image conversion may be based on one or more colour channels including the red channel R and optionally the blue channel and/or the green channel of the image being converted. The converted image I_C may be given as:
I_C=Abs(R−Average(G−B),
where R is the red channel in the image, G is the green channel, B is the blue channel. In other words, the red, blue, and green channels of the image may be converted into a single combined channel also denoted CC for each pixel of the image.
In one or more exemplary methods, obtaining image data comprises:
In one or more exemplary methods, obtaining image data comprises:
Providing feedback to the user may comprise displaying a user interface element on a display of the accessory device. Providing feedback to the user may comprise determining a property of the user interface element based on the position of the accessory device in relation to the adhesive surface of the ostomy appliance.
In one or more exemplary methods, the method comprises, after providing feedback to the user:
In one or more exemplary methods, determining a position of the accessory device in relation to the adhesive surface of the ostomy appliance comprises determining an angle between an optical axis of the camera and a proximal surface of the base plate.
In one or more exemplary methods, determining a position of the accessory device in relation to the stomal area comprises determining an angle between an optical axis of the camera and a reference surface of the stomal area, the reference surface being perpendicular to the axial direction.
In one or more exemplary methods, determining a position of the accessory device in relation to the adhesive surface of the ostomy appliance comprises determining a distance between the accessory device and the adhesive surface.
In one or more exemplary methods, the method comprises obtaining an ostomy appliance configuration, e.g. including an ostomy appliance identifier, and wherein determining a position of the accessory device in relation to the adhesive surface of the ostomy appliance is based on the ostomy appliance configuration.
Determining S104 one or more ostomy representations optionally comprises determining S104B one or more image representations based on the image data or transformed image data; and determining S104C one or more ostomy representations including a first ostomy parameter based on the one or more image representations.
Determining S104B one or more image representations based on the image data or transformed image data optionally comprises determining S104BA one or more stoma image representations indicative of the stomal area and optionally determining S104BB one or more appliance image representations indicative of the adhesive surface of the ostomy appliance.
In one or more exemplary methods, the one or more stoma image representations comprises at least four stoma image representations including a first stoma image representation SIR_1, a second stoma image representation SIR_2, a third stoma image representation SIR_3, and a fourth stoma image representation SIR_4.
The first stoma image representation may be a stoma background image representation indicative of a background of the stoma image data, i.e. which part(s)/pixels of the stoma image data that are regarded or identified as background, i.e. outside the area covered by the adhesive surface (e.g. including part of user skin not covered by adhesive surface of ostomy appliance).
The second stoma image representation may be a stoma image representation indicative of the stoma, i.e. which part(s)/pixels of the ostomy image data that are regarded or identified as the stoma.
The third stoma image representation may be a normal skin image representation indicative of the normal skin of the peristomal area, i.e. which part(s)/pixels of the ostomy image data that are regarded or identified as not having discoloration.
The fourth stoma image representation may be a first discoloration representation indicative of a discoloration of the peristomal area, i.e. which part(s)/pixels of the ostomy image data that are regarded or identified as the peristomal area and discoloured.
In one or more exemplary methods, the one more appliance image representations comprises at least three or at least four appliance image representations including a first appliance image representation AIR_1, optionally a second appliance image representation AIR_2, a third appliance image representation AIR_3, and a fourth appliance image representation AIR_4.
The first appliance image representation may be an appliance background image representation indicative of a background of the appliance image data, i.e. which part(s)/pixels of the appliance image data/transformed appliance image data that are regarded or identified as background (e.g. image part(s)/pixel(s) outside the area of the adhesive surface of the ostomy appliance).
The second appliance image representation may be a stomal opening image representation indicative of the stomal opening, i.e. which part(s)/pixels of the appliance image data/transformed appliance image data that are regarded or identified as the stomal opening.
The third appliance image representation may be an appliance area representation indicative of no appliance discoloration on the adhesive surface, i.e. no leak of output and thus which part(s)/pixels of the appliance image data/transformed appliance image data that are regarded or identified as the adhesive surface and not being discoloured by output.
The fourth appliance image representation may be a first appliance discoloration representation indicative of a discoloration of the adhesive surface of the ostomy appliance, i.e. which part(s)/pixels of the appliance image data that are regarded or identified as the adhesive surface and have a discoloration (leak of output).
Determining S104C one or more ostomy representations including a first ostomy parameter based on the one or more image representations optionally comprises determining S104CA a first ostomy representation OR_1 comprising the first ostomy parameter OP_1 based on one or more stoma image representations.
In one or more exemplary methods, the first ostomy representation OR_1 may be based on the first stoma image representation SIR_1, the second stoma image representation SIR_2, the third stoma image representation SIR_3, and the fourth stoma image representation SIR_4. The first ostomy representation OR_1 may comprise or be based on the ostomy image data/transformed ostomy image data.
Determining S104CA the first ostomy representation optionally comprises determining a second ostomy parameter OP_2 or a set of second ostomy parameters. In other words, the first ostomy representation OR_1 may comprise OP_1, and one or more second ostomy parameters.
Determining S104C one or more ostomy representations including a first ostomy parameter based on the one or more image representations optionally comprises determining S104CB a second ostomy representation OR_2 based on one or more appliance image representations.
In one or more exemplary methods, the second ostomy representation OR_2 may be based on the first appliance image representation, optionally the second appliance image representation, the third appliance image representation, and the fourth appliance image representation.
Outputting S106 the first ostomy parameter may comprise storing S106A the first ostomy parameter or a first ostomy representation comprising the first ostomy parameter in a memory and/or transmitting S106B the first ostomy parameter or a first ostomy representation comprising the first ostomy parameter to an accessory device and/or server device. Outputting S106 the first ostomy parameter may comprise displaying S106C the first ostomy parameter or a first ostomy representation comprising the first ostomy parameter on a display of an accessory device. Thereby a user and/or professional caretaker is able to verify and act on an ostomy condition in substantially real-time. For example, a user is able to or can be prompted to take measures to reduce the effects of an ostomy condition when changing the ostomy appliance, e.g. during a changing procedure in substantially real-time.
The method 100 comprises outputting 110 one or more ostomy representations, e.g. including the first ostomy representation OR_1 and/or the second ostomy representation OR_2. Outputting 110 one of more ostomy representations may comprise outputting 112 a second ostomy representation. Outputting S112 the second ostomy representation may comprise storing S112A the second ostomy representation in a memory and/or transmitting S112B the second ostomy representation to an accessory device and/or server device. Outputting S112 the second ostomy representation may comprise displaying S112C the second ostomy representation on a display of an accessory device. Thereby a user and/or professional caretaker is able to verify and act on an ostomy condition in substantially real-time. For example, a user is able to or can be prompted to take measures to reduce the effects of an ostomy condition when changing the ostomy appliance, e.g. during a changing procedure in substantially real-time.
Transforming S108 the image data comprises determining S108A a position parameter representative of a position of a camera image plane in relation to the stomal area and/or the adhesive surface, and wherein the transformed image data are based on the position parameter.
The position parameter optionally comprises an angle parameter representative of an angle between an optical axis of a camera being the source of the image data and an axial direction of the stomal area/normal to the adhesive surface and wherein the transformed image data are based on the angle parameter. Thus, determining S108A a position parameter may comprise determining 108B an angle parameter representative of an angle between an optical axis of a camera being the source of the image data and an axial direction of the stomal area/normal to the adhesive surface, and wherein the transformed image data are based on the angle parameter.
The position parameter optionally comprises a distance parameter representative of a distance between a camera being the source of the image data and the stomal area/adhesive surface, and wherein the transformed image data are based on the distance parameter. Thus, determining S108A a position parameter may comprise determining 108C a distance parameter representative of a distance between a camera being the source of the image data and the stomal area/adhesive surface, and wherein the transformed image data are based on the distance parameter.
The position parameter optionally comprises a rotation parameter representative of a rotational angle between an image axis of the image data and a reference axis of the stomal area/adhesive surface. Thus, determining S108A a position parameter may comprise determining 108D a rotation parameter representative of a rotational angle between an image axis of the image data and a reference axis of the stomal area/adhesive surface, and wherein the transformed image data are based on the rotation parameter.
In method 100, transforming S108 the image data optionally comprises identifying S108E one or more reference indicators of the image data, e.g. a first stoma reference indicator and/or a second stoma reference indicator of the stoma image data and/or a first appliance reference indicator and/or a second appliance reference indicator of the appliance image data. The transformed image data are optionally based on the reference indicator(s).
In method 100, transforming S108 the image data optionally comprises scaling S108F the image data to a predetermined pixel size, e.g. to a pixel size of 256×256 pixels. Scaling the image data comprises determining a scaling parameter, and wherein the transformed image data are based on the scaling parameter.
In method 100, transforming S108 the image data optionally comprises centering 108G the image data about a center or center region of the image data, e.g. based on a reference indicator (position and/or direction) of respective stoma image data and/or appliance image data. For example, transforming stoma image data may comprise centering the stoma image data about a center, perimeter, or center region of the stoma (e.g. identified as a first or second stoma reference identifier in S108E). In one or more exemplary methods, transforming appliance image data may comprise centering the appliance image data about a center, perimeter, or center region of the stomal opening of the ostomy appliance/baseplate and/or about a perimeter of the adhesive surface/baseplate of the ostomy appliance (e.g. identified as a first or second appliance reference identifier in S108E).
The operations of the accessory device 200 may be embodied in the form of executable logic routines (e.g., lines of code, software programs, etc.) that are stored on a non-transitory computer readable medium (e.g., the memory module 301) and are executed by the processor module 302. Furthermore, the operations of the accessory device 200 may be considered a method that the accessory device 200 is configured to carry out. Also, while the described functions and operations may be implemented in software, such functionality may as well be carried out via dedicated hardware or firmware, or some combination of hardware, firmware and/or software.
The memory module 301 may be one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, a random access memory (RAM), or other suitable device. In a typical arrangement, the memory module 301 may include a non-volatile memory for long term data storage and a volatile memory that functions as system memory for the processor module 302. The memory module 301 may exchange data with the processor module 302 over a data bus. Control lines and an address bus between the memory module 301 and the processor module 302 also may be present (not shown in
The server device comprises a memory module 401, a processor module 402, and an interface 403. The server device 208 is configured to perform any of the methods disclosed herein, such as any of the methods shown in
The operations of the server device 208 may be embodied in the form of executable logic routines (e.g., lines of code, software programs, etc.) that are stored on a non-transitory computer readable medium (e.g., the memory module 401) and are executed by the processor module 402. Furthermore, the operations of the server device 208 may be considered a method that the server device 208 is configured to carry out. Also, while the described functions and operations may be implemented in software, such functionality may as well be carried out via dedicated hardware or firmware, or some combination of hardware, firmware and/or software.
The memory module 401 may be one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, a random access memory (RAM), or other suitable device. In a typical arrangement, the memory module 401 may include a non-volatile memory for long term data storage and a volatile memory that functions as system memory for the processor module 402. The memory module 401 may exchange data with the processor module 402 over a data bus. Control lines and an address bus between the memory module 401 and the processor module 402 also may be present (not shown in
The first ostomy representation OR_1 comprises first boundary line BL_1 (red line) indicative of a circumference or edge of the stomal area, e.g. indicative of a boundary between the normal skin area 450 and background 454 of the stoma image data. The first boundary line is based on the first stoma image representation and/or the third stoma image representation.
The first ostomy representation OR_1 comprises second boundary line BL_2 (green line) indicative of a circumference or edge of the stoma 456, wherein the second boundary line is based on the second stoma image representation and/or the fourth stoma image representation.
The first ostomy representation OR_1 comprises third boundary lines BL_3 (blue lines) indicative of a boundary between a normal skin area 450 of the peristomal area (non-discoloured) and a discoloured area 452 of the peristomal area. The third boundary lines BL_3 are based on the third stoma image representation and/or the fourth stoma image representation.
The first ostomy representation OR_1 comprises or is overlaid on the stoma image data SID on which the first ostomy representation OP_1 is based.
The second appliance image representation AIR_2 of 256×256 pixels is a stomal opening image representation indicative of the stomal opening 460, i.e. which part(s)/pixels of the appliance image data/transformed appliance image data that are regarded or identified as the stomal opening. Yellow represents the binary value 1 of the binary mask (i.e. pixel is part of stomal opening) and purple represents the binary value 0 (i.e. pixel is not part of stomal opening).
The third appliance image representation AIR_3 of 256×256 pixels is an appliance area representation indicative of no appliance discoloration (clean adhesive surface 462) on the adhesive surface of the ostomy appliance, i.e. no leak of output and thus which part(s)/pixels of the appliance image data/transformed appliance image data that are regarded or identified as the adhesive surface and not being discoloured by output. Yellow represents the binary value 1 of the binary mask (i.e. pixel is not discoloured) and purple represents the binary value 0 (i.e. pixel is not part of non-discoloured adhesive surface).
The fourth appliance image representation AIR_4 of 256×256 pixels is a first appliance discoloration representation indicative of a discoloration (discoloured adhesive surface 464) of the adhesive surface of the ostomy appliance, i.e. which part(s)/pixels of the appliance image data that are regarded or identified as the adhesive surface and have a discoloration (leak of output). Yellow represents the binary value 1 of the binary mask (i.e. pixel is discoloured) and purple represents the binary value 0 (i.e. pixel is not part of discoloured adhesive surface).
The second ostomy representation OR_2 comprises second boundary line BL_2 (green line) indicative of a circumference or edge of the stomal opening of the adhesive surface, wherein the second boundary line is based on the second appliance image representation and/or the fourth appliance image representation.
The second ostomy representation OR_2 comprises third boundary line BL_3 (blue line) indicative of a boundary between a discoloured part (output leak) and a non-discoloured part (clean) of the adhesive surface. The third boundary lines BL_3 are based on the third stoma image representation and/or the fourth stoma image representation.
The second ostomy representation OR_2 comprises or is overlaid on the appliance image data AID on which the second ostomy representation OP_2 is based.
Also disclosed are methods according to any of the following items.
Item 1. Methods for classifying an ostomy condition, the method comprising:
obtaining image data, the image data comprising stoma image data of a stomal area including a stoma and/or appliance image data of an adhesive surface of an ostomy appliance;
determining one or more ostomy representations including a first ostomy parameter based on the image data; and
outputting the first ostomy parameter, wherein the method comprises transforming the image data, and wherein determining the one or more ostomy representations based on the image data comprises determining the first ostomy parameter based on the transformed image data.
Item 2. Method according to item 1, wherein transforming the image data comprises determining a position parameter representative of a position of a camera image plane in relation to the stomal area and/or the adhesive surface, and wherein the transformed image data are based on the position parameter.
Item 3. Method according to item 2, wherein the position parameter comprises an angle parameter representative of an angle between an optical axis of a camera being the source of the image data and an axial direction of the stomal area/normal to the adhesive surface and wherein the transformed image data are based on the angle parameter.
Item 4. Method according to any of items 2-3, wherein the position parameter comprises a distance parameter representative of a distance between a camera being the source of the image data and the stomal area/adhesive surface, and wherein the transformed image data are based on the distance parameter.
Item 5. Method according to any of items 2-4, wherein the position parameter comprises a rotation parameter representative of a rotational angle between an image axis of the image data and a reference axis of the stomal area/adhesive surface, and wherein the transformed image data are based on the rotation parameter.
Item 6. Method according to any of items 1-5, wherein transforming the image data comprises fitting the image data to a stomal area model image and/or an appliance model image.
Item 7. Method according to any of items 1-6, wherein transforming the image data comprises identifying a first stoma reference indicator on the stomal area, and wherein the transformed image data are based on the first stoma reference indicator.
Item 8. Method according to item 7, wherein the first stoma reference indicator is a perimeter of the stoma.
Item 9. Method according to any of items 1-8, wherein transforming the image data comprises identifying a first appliance reference indicator on the adhesive surface of the ostomy appliance, and wherein the transformed image data are based on the first appliance reference indicator.
Item 10. Method according to item 9, wherein the first appliance reference indicator is a perimeter of the ostomy appliance.
Item 11. Method according to any of items 1-10, wherein transforming the image data comprises identifying a second appliance reference indicator on the adhesive surface of the ostomy appliance, wherein the transformed image data are based on the second appliance reference indicator, and wherein the second appliance reference indicator is an edge of a stomal opening of the ostomy appliance.
Item 12. Method according to any of items 1-11, wherein transforming the image data comprises scaling the image data to a predetermined pixel size.
Item 13. Method according to item 12, wherein scaling the image data comprises determining a scaling parameter, and wherein the transformed image data are based on the scaling parameter.
Item 14. Method according to any of items 1-13, wherein the first ostomy parameter is a discoloration index indicative of discoloration of the stomal area.
Item 15. Method according to any of items 1-14, wherein the first ostomy parameter is a leakage parameter indicative of output distribution on the adhesive surface.
The use of the terms “first”, “second”, “third” and “fourth”, “primary”, “secondary”, “tertiary” etc. does not imply any particular order, but are included to identify individual elements. Moreover, the use of the terms “first”, “second”, “third” and “fourth”, “primary”, “secondary”, “tertiary” etc. does not denote any order or importance, but rather the terms “first”, “second”, “third” and “fourth”, “primary”, “secondary”, “tertiary” etc. are used to distinguish one element from another. Note that the words “first”, “second”, “third” and “fourth”, “primary”, “secondary”, “tertiary” etc. are used here and elsewhere for labelling purposes only and are not intended to denote any specific spatial or temporal ordering.
Furthermore, the labelling of a first element does not imply the presence of a second element and vice versa.
It may be appreciated that
It is to be noted that the word “comprising” does not necessarily exclude the presence of other elements or steps than those listed.
It is to be noted that the words “a” or “an” preceding an element do not exclude the presence of a plurality of such elements.
It should further be noted that any reference signs do not limit the scope of the claims, that the exemplary embodiments may be implemented at least in part by means of both hardware and software, and that several “means”, “units” or “devices” may be represented by the same item of hardware.
The various exemplary methods, devices, and systems described herein are described in the general context of method steps processes, which may be implemented in one aspect by a computer program product, embodied in a computer-readable medium, including computer-executable instructions, such as program code, executed by computers in networked environments. A computer-readable medium may include removable and non-removable storage devices including, but not limited to, Read Only Memory (ROM), Random Access Memory (RAM), compact discs (CDs), digital versatile discs (DVD), etc. Generally, program modules may include routines, programs, objects, components, data structures, etc. that perform specified tasks or implement specific abstract data types.
Computer-executable instructions, associated data structures, and program modules represent examples of program code for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps or processes.
Although features have been shown and described, it will be understood that they are not intended to limit the claimed invention, and it will be made obvious to those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the claimed invention. The specification and drawings are, accordingly to be regarded in an illustrative rather than restrictive sense. The claimed invention is intended to cover all alternatives, modifications, and equivalents.
| Number | Date | Country | Kind |
|---|---|---|---|
| PA 2018 70832 | Dec 2018 | DK | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/DK2019/050416 | 12/19/2019 | WO |
| Publishing Document | Publishing Date | Country | Kind |
|---|---|---|---|
| WO2020/125907 | 6/25/2020 | WO | A |
| Number | Name | Date | Kind |
|---|---|---|---|
| 2327514 | Fenwick | Aug 1943 | A |
| 2542233 | Carroll | Feb 1951 | A |
| 2544579 | Ardner | Mar 1951 | A |
| 3214502 | Schaar | Oct 1965 | A |
| 3808354 | Feezor et al. | Apr 1974 | A |
| 3832510 | Pfau et al. | Aug 1974 | A |
| 3915171 | Shermeta | Oct 1975 | A |
| 3941133 | Chen | Mar 1976 | A |
| 4231369 | Sorensen et al. | Nov 1980 | A |
| 4372308 | Steer et al. | Feb 1983 | A |
| 4449970 | Bevan et al. | May 1984 | A |
| 4668227 | Kay | May 1987 | A |
| 4754264 | Okada et al. | Jun 1988 | A |
| 4775374 | Cilento et al. | Oct 1988 | A |
| 4834731 | Nowak et al. | May 1989 | A |
| 4973323 | Kaczmarek et al. | Nov 1990 | A |
| 4982742 | Claude | Jan 1991 | A |
| 5013307 | Broida | May 1991 | A |
| 5016645 | Williams et al. | May 1991 | A |
| 5051259 | Olsen et al. | Sep 1991 | A |
| 5074851 | Plass et al. | Dec 1991 | A |
| 5111812 | Swanson et al. | May 1992 | A |
| 5237995 | Cano | Aug 1993 | A |
| 5318543 | Ross et al. | Jun 1994 | A |
| 5358488 | Suriyapa | Oct 1994 | A |
| 5486158 | Samuelsen | Jan 1996 | A |
| 5519644 | Benton | May 1996 | A |
| 5570082 | Mahgerefteh et al. | Oct 1996 | A |
| 5593397 | La Gro | Jan 1997 | A |
| 5626135 | Sanfilippo | May 1997 | A |
| 5672163 | Ferreira et al. | Sep 1997 | A |
| 5677221 | Tseng | Oct 1997 | A |
| 5704905 | Jensen et al. | Jan 1998 | A |
| 5790036 | Fisher et al. | Aug 1998 | A |
| 5800415 | Olsen | Sep 1998 | A |
| 5816252 | Faries et al. | Oct 1998 | A |
| 5834009 | Sawers et al. | Nov 1998 | A |
| 5879292 | Sternberg et al. | Mar 1999 | A |
| 5942186 | Sanada et al. | Aug 1999 | A |
| 6015399 | Mracna et al. | Jan 2000 | A |
| 6025725 | Gershenfeld et al. | Feb 2000 | A |
| 6078261 | Davsko | Jun 2000 | A |
| 6103033 | Say et al. | Aug 2000 | A |
| 6135986 | Leisner et al. | Oct 2000 | A |
| 6171289 | Millot et al. | Jan 2001 | B1 |
| 6206864 | Kavanagh et al. | Mar 2001 | B1 |
| 6241704 | Peterson et al. | Jun 2001 | B1 |
| 6270445 | Dean, Jr. et al. | Aug 2001 | B1 |
| 6407308 | Roe et al. | Jun 2002 | B1 |
| 6433244 | Roe et al. | Aug 2002 | B1 |
| 6482491 | Samuelsen et al. | Nov 2002 | B1 |
| 6485476 | Von et al. | Nov 2002 | B1 |
| 6520943 | Wagner | Feb 2003 | B1 |
| 6677859 | Bensen | Jan 2004 | B1 |
| 6764474 | Nielsen et al. | Jul 2004 | B2 |
| 7066919 | Sauerland et al. | Jun 2006 | B1 |
| 7150728 | Hansen et al. | Dec 2006 | B2 |
| 7166091 | Zeltner | Jan 2007 | B1 |
| 7199501 | Pei et al. | Apr 2007 | B2 |
| 7214217 | Pedersen et al. | May 2007 | B2 |
| 7326190 | Botten | Feb 2008 | B2 |
| 7341578 | Bulow et al. | Mar 2008 | B2 |
| 7347844 | Cline et al. | Mar 2008 | B2 |
| 7367965 | Poulsen et al. | May 2008 | B2 |
| 7559922 | Botten | Jul 2009 | B2 |
| 7625362 | Boehringer et al. | Dec 2009 | B2 |
| 7641612 | McCall | Jan 2010 | B1 |
| 7670289 | McCall | Mar 2010 | B1 |
| 7943812 | Stroebeck et al. | May 2011 | B2 |
| 7981098 | Boehringer et al. | Jul 2011 | B2 |
| 8061360 | Locke et al. | Nov 2011 | B2 |
| 8277427 | Edvardsen et al. | Oct 2012 | B2 |
| 8319003 | Olsen et al. | Nov 2012 | B2 |
| 8326051 | Hobbs | Dec 2012 | B1 |
| 8398575 | McCall | Mar 2013 | B1 |
| 8398603 | Thirstrup et al. | Mar 2013 | B2 |
| 8399732 | Oelund et al. | Mar 2013 | B2 |
| 8409158 | Edvardsen et al. | Apr 2013 | B2 |
| 8449471 | Tran | May 2013 | B2 |
| 8500718 | Locke et al. | Aug 2013 | B2 |
| 8632492 | Delegge | Jan 2014 | B2 |
| 8680991 | Tran | Mar 2014 | B2 |
| 8684982 | Nguyen-Demary et al. | Apr 2014 | B2 |
| 8740865 | Krystek et al. | Jun 2014 | B2 |
| 8795257 | Coulthard et al. | Aug 2014 | B2 |
| 8821464 | Hanuka et al. | Sep 2014 | B2 |
| 8975465 | Hong et al. | Mar 2015 | B2 |
| 9046085 | Schoess et al. | Jun 2015 | B2 |
| 9066812 | Edvardsen et al. | Jun 2015 | B2 |
| 9216104 | Thirstrup et al. | Dec 2015 | B2 |
| 9308332 | Heppe | Apr 2016 | B2 |
| 9322797 | Lastinger et al. | Apr 2016 | B1 |
| 9566383 | Yodfat et al. | Feb 2017 | B2 |
| 9629964 | Wuepper | Apr 2017 | B2 |
| 9675267 | Laakkonen et al. | Jun 2017 | B2 |
| 9693908 | Eriksson et al. | Jul 2017 | B2 |
| 9770359 | Edvardsen et al. | Sep 2017 | B2 |
| 9788991 | Bird | Oct 2017 | B2 |
| 9867934 | Heppe | Jan 2018 | B2 |
| 9928341 | Angelides | Mar 2018 | B2 |
| 10016298 | Thirstrup et al. | Jul 2018 | B2 |
| D826740 | Stevens et al. | Aug 2018 | S |
| 10426342 | Hresko et al. | Oct 2019 | B2 |
| 10500084 | Hansen et al. | Dec 2019 | B2 |
| 10531977 | Schoess et al. | Jan 2020 | B2 |
| 10646370 | Keleny et al. | May 2020 | B2 |
| 10792184 | Hvid et al. | Oct 2020 | B2 |
| 10799385 | Hansen et al. | Oct 2020 | B2 |
| 10849781 | Hansen et al. | Dec 2020 | B2 |
| 10874541 | Seres et al. | Dec 2020 | B2 |
| 10987243 | Thirstrup et al. | Apr 2021 | B2 |
| 11096818 | Thirstrup et al. | Aug 2021 | B2 |
| 11135084 | Seres et al. | Oct 2021 | B2 |
| 11238133 | Brewer et al. | Feb 2022 | B1 |
| 11306224 | Chatterjee et al. | Apr 2022 | B2 |
| 11406525 | Seres et al. | Aug 2022 | B2 |
| 11471318 | Hansen et al. | Oct 2022 | B2 |
| 11612512 | Hansen et al. | Mar 2023 | B2 |
| 20010041920 | Starkweather et al. | Nov 2001 | A1 |
| 20010051787 | Haller et al. | Dec 2001 | A1 |
| 20020013613 | Haller et al. | Jan 2002 | A1 |
| 20020019615 | Roe et al. | Feb 2002 | A1 |
| 20020109621 | Khair et al. | Aug 2002 | A1 |
| 20030132763 | Ellenz | Jul 2003 | A1 |
| 20030169032 | Minchole et al. | Sep 2003 | A1 |
| 20040006320 | Buglino et al. | Jan 2004 | A1 |
| 20040030305 | Sakamoto | Feb 2004 | A1 |
| 20040036484 | Tamai | Feb 2004 | A1 |
| 20040049145 | Flick | Mar 2004 | A1 |
| 20040078219 | Kaylor et al. | Apr 2004 | A1 |
| 20040100376 | Lye et al. | May 2004 | A1 |
| 20040106908 | Leise et al. | Jun 2004 | A1 |
| 20040133175 | Hagedorn-Olsen | Jul 2004 | A1 |
| 20040171999 | Andersen et al. | Sep 2004 | A1 |
| 20040193122 | Cline et al. | Sep 2004 | A1 |
| 20040193123 | Fenton | Sep 2004 | A1 |
| 20040216833 | Fleming et al. | Nov 2004 | A1 |
| 20050054997 | Buglino et al. | Mar 2005 | A1 |
| 20050065488 | Elliott | Mar 2005 | A1 |
| 20050070863 | Bulow et al. | Mar 2005 | A1 |
| 20050085779 | Poulsen et al. | Apr 2005 | A1 |
| 20050101841 | Kaylor et al. | May 2005 | A9 |
| 20050240163 | Andersen | Oct 2005 | A1 |
| 20050256545 | Koh et al. | Nov 2005 | A1 |
| 20050261645 | Conrad et al. | Nov 2005 | A1 |
| 20060015081 | Suzuki et al. | Jan 2006 | A1 |
| 20060025727 | Boehringer et al. | Feb 2006 | A1 |
| 20060052752 | McMichael | Mar 2006 | A1 |
| 20060194324 | Faries et al. | Aug 2006 | A1 |
| 20060271002 | Botten | Nov 2006 | A1 |
| 20070035405 | Wada et al. | Feb 2007 | A1 |
| 20070135782 | Bager et al. | Jun 2007 | A1 |
| 20070185464 | Fattman et al. | Aug 2007 | A1 |
| 20070204691 | Bogner et al. | Sep 2007 | A1 |
| 20080038536 | Strobech et al. | Feb 2008 | A1 |
| 20080041792 | Crnkovich et al. | Feb 2008 | A1 |
| 20080071214 | Locke et al. | Mar 2008 | A1 |
| 20080075934 | Barlow et al. | Mar 2008 | A1 |
| 20080091154 | Botten | Apr 2008 | A1 |
| 20080140057 | Wood et al. | Jun 2008 | A1 |
| 20080234641 | Locke et al. | Sep 2008 | A1 |
| 20080275327 | Faarbaek et al. | Nov 2008 | A1 |
| 20080278337 | Huang et al. | Nov 2008 | A1 |
| 20080300559 | Gustafson et al. | Dec 2008 | A1 |
| 20080300578 | Freedman | Dec 2008 | A1 |
| 20080306459 | Albrectsen | Dec 2008 | A1 |
| 20090012501 | Boehringer et al. | Jan 2009 | A1 |
| 20090118687 | Kristensen et al. | May 2009 | A1 |
| 20090167286 | Naylor et al. | Jul 2009 | A1 |
| 20090173935 | Cho et al. | Jul 2009 | A1 |
| 20090227969 | Jaeb et al. | Sep 2009 | A1 |
| 20090234916 | Cosentino et al. | Sep 2009 | A1 |
| 20090247970 | Keleny et al. | Oct 2009 | A1 |
| 20090264957 | Giftakis et al. | Oct 2009 | A1 |
| 20100010460 | Butler | Jan 2010 | A1 |
| 20100030167 | Thirstrup et al. | Feb 2010 | A1 |
| 20100072271 | Thorstensson | Mar 2010 | A1 |
| 20100106220 | Ecker et al. | Apr 2010 | A1 |
| 20100114047 | Song et al. | May 2010 | A1 |
| 20100271212 | Page | Oct 2010 | A1 |
| 20100311167 | Wood et al. | Dec 2010 | A1 |
| 20110034890 | Stroebech et al. | Feb 2011 | A1 |
| 20110077497 | Oster et al. | Mar 2011 | A1 |
| 20110130642 | Jaeb et al. | Jun 2011 | A1 |
| 20110245682 | Robinson et al. | Oct 2011 | A1 |
| 20110246983 | Brunet et al. | Oct 2011 | A1 |
| 20110257496 | Terashima et al. | Oct 2011 | A1 |
| 20120013130 | Jung | Jan 2012 | A1 |
| 20120143154 | Edvardsen et al. | Jun 2012 | A1 |
| 20120143155 | Edvardsen et al. | Jun 2012 | A1 |
| 20120253224 | Mir et al. | Oct 2012 | A1 |
| 20120258302 | Hunt et al. | Oct 2012 | A1 |
| 20120283678 | Nguyen-Demary et al. | Nov 2012 | A1 |
| 20120304767 | Howard et al. | Dec 2012 | A1 |
| 20130018231 | Hong et al. | Jan 2013 | A1 |
| 20130030167 | Wang et al. | Jan 2013 | A1 |
| 20130030397 | Sabeti | Jan 2013 | A1 |
| 20130060213 | Hanuka et al. | Mar 2013 | A1 |
| 20130066285 | Locke et al. | Mar 2013 | A1 |
| 20130072886 | Schertiger et al. | Mar 2013 | A1 |
| 20130078912 | San Vicente et al. | Mar 2013 | A1 |
| 20130086217 | Price et al. | Apr 2013 | A1 |
| 20130102979 | Coulthard et al. | Apr 2013 | A1 |
| 20130138065 | Buus | May 2013 | A1 |
| 20130150769 | Heppe | Jun 2013 | A1 |
| 20130165862 | Griffith et al. | Jun 2013 | A1 |
| 20130192604 | Persson et al. | Aug 2013 | A1 |
| 20130226116 | Edvardsen et al. | Aug 2013 | A1 |
| 20130231620 | Thirstrup et al. | Sep 2013 | A1 |
| 20130254141 | Barda et al. | Sep 2013 | A1 |
| 20130303867 | Elfstrom et al. | Nov 2013 | A1 |
| 20130307570 | Bosaeus et al. | Nov 2013 | A1 |
| 20130324952 | Krystek et al. | Dec 2013 | A1 |
| 20130324955 | Wong et al. | Dec 2013 | A1 |
| 20140051946 | Arne et al. | Feb 2014 | A1 |
| 20140128815 | Cabiri et al. | May 2014 | A1 |
| 20140200426 | Taub et al. | Jul 2014 | A1 |
| 20140200538 | Euliano et al. | Jul 2014 | A1 |
| 20140236111 | Casado et al. | Aug 2014 | A1 |
| 20140275854 | Venkatraman et al. | Sep 2014 | A1 |
| 20140276501 | Cisko | Sep 2014 | A1 |
| 20140288381 | Faarbaek et al. | Sep 2014 | A1 |
| 20140309600 | Aceto et al. | Oct 2014 | A1 |
| 20140323909 | Kim | Oct 2014 | A1 |
| 20140327433 | Anway et al. | Nov 2014 | A1 |
| 20140336493 | Kulach et al. | Nov 2014 | A1 |
| 20150057634 | Mastrototaro et al. | Feb 2015 | A1 |
| 20150150457 | Wu et al. | Jun 2015 | A1 |
| 20150151051 | Tsoukalis | Jun 2015 | A1 |
| 20150230706 | Nakagawa et al. | Aug 2015 | A1 |
| 20150231802 | Quan et al. | Aug 2015 | A1 |
| 20150250639 | Thirstrup et al. | Sep 2015 | A1 |
| 20150257923 | Thirstrup et al. | Sep 2015 | A1 |
| 20150328389 | Heppe | Nov 2015 | A1 |
| 20150342777 | Seres et al. | Dec 2015 | A1 |
| 20150374896 | Du et al. | Dec 2015 | A1 |
| 20160008182 | Prokopuk et al. | Jan 2016 | A1 |
| 20160058604 | Wiltshire et al. | Mar 2016 | A1 |
| 20160084869 | Yuen et al. | Mar 2016 | A1 |
| 20160103966 | Mirza | Apr 2016 | A1 |
| 20160117062 | Hussam et al. | Apr 2016 | A1 |
| 20160158056 | Davis et al. | Jun 2016 | A1 |
| 20160158517 | Nebbia | Jun 2016 | A1 |
| 20160158969 | McLane et al. | Jun 2016 | A1 |
| 20160166438 | Rovaniemi | Jun 2016 | A1 |
| 20160178387 | Yamasaki et al. | Jun 2016 | A1 |
| 20160218555 | Slaby et al. | Jul 2016 | A1 |
| 20160235581 | Keleny et al. | Aug 2016 | A1 |
| 20160242654 | Quinlan et al. | Aug 2016 | A1 |
| 20160278990 | Chen | Sep 2016 | A1 |
| 20160305776 | Mrtensson et al. | Oct 2016 | A1 |
| 20160310140 | Belson et al. | Oct 2016 | A1 |
| 20160310329 | Patel et al. | Oct 2016 | A1 |
| 20160331232 | Love et al. | Nov 2016 | A1 |
| 20160361015 | Wang et al. | Dec 2016 | A1 |
| 20170042614 | Salahieh et al. | Feb 2017 | A1 |
| 20170050004 | Tilson et al. | Feb 2017 | A1 |
| 20170055896 | Al-Ali et al. | Mar 2017 | A1 |
| 20170079576 | Stroebech et al. | Mar 2017 | A1 |
| 20170098044 | Lai et al. | Apr 2017 | A1 |
| 20170113001 | Trock | Apr 2017 | A1 |
| 20170140103 | Angelides | May 2017 | A1 |
| 20170156920 | Hunt et al. | Jun 2017 | A1 |
| 20170181628 | Burnette et al. | Jun 2017 | A1 |
| 20170340474 | Thirstrup et al. | Nov 2017 | A1 |
| 20170340498 | Tessmer et al. | Nov 2017 | A1 |
| 20170348137 | Hvid et al. | Dec 2017 | A1 |
| 20170348162 | Arizti et al. | Dec 2017 | A1 |
| 20170360592 | Carrubba | Dec 2017 | A1 |
| 20170360593 | Cox | Dec 2017 | A1 |
| 20180049667 | Heppe | Feb 2018 | A1 |
| 20180055359 | Shamim et al. | Mar 2018 | A1 |
| 20180110078 | Mandapaka et al. | Apr 2018 | A1 |
| 20180136712 | Niikura et al. | May 2018 | A1 |
| 20180171183 | Sakurai et al. | Jun 2018 | A1 |
| 20180298240 | Chatterjee et al. | Oct 2018 | A1 |
| 20180318475 | Thomson et al. | Nov 2018 | A1 |
| 20190008439 | Sageder et al. | Jan 2019 | A1 |
| 20190133810 | Seres et al. | May 2019 | A1 |
| 20190133811 | Seres et al. | May 2019 | A1 |
| 20190133812 | Seres et al. | May 2019 | A1 |
| 20190142623 | Schoess et al. | May 2019 | A1 |
| 20190175386 | Monty | Jun 2019 | A1 |
| 20190184093 | Sjolund et al. | Jun 2019 | A1 |
| 20190192066 | Schoess et al. | Jun 2019 | A1 |
| 20190192332 | Hansen et al. | Jun 2019 | A1 |
| 20190192333 | Hansen et al. | Jun 2019 | A1 |
| 20190192334 | Hansen et al. | Jun 2019 | A1 |
| 20190240059 | Seres et al. | Aug 2019 | A1 |
| 20190247050 | Goldsmith | Aug 2019 | A1 |
| 20190374163 | Faarbaek et al. | Dec 2019 | A1 |
| 20200100931 | Schoess et al. | Apr 2020 | A1 |
| 20200188161 | Seres et al. | Jun 2020 | A1 |
| 20200246174 | Hansen et al. | Aug 2020 | A1 |
| 20200246175 | Hansen et al. | Aug 2020 | A1 |
| 20200246176 | Hansen et al. | Aug 2020 | A1 |
| 20200246177 | Hansen et al. | Aug 2020 | A1 |
| 20200276063 | Muñoz Herencia | Sep 2020 | A1 |
| 20200279368 | Tada et al. | Sep 2020 | A1 |
| 20200297244 | Brownhill et al. | Sep 2020 | A1 |
| 20200306074 | Speiermann et al. | Oct 2020 | A1 |
| 20200322793 | Yang | Oct 2020 | A1 |
| 20200330258 | Hansen et al. | Oct 2020 | A1 |
| 20200330260 | Hansen et al. | Oct 2020 | A1 |
| 20200337880 | Hansen et al. | Oct 2020 | A1 |
| 20200337881 | Hansen et al. | Oct 2020 | A1 |
| 20200337882 | Hansen et al. | Oct 2020 | A1 |
| 20200337883 | Hansen et al. | Oct 2020 | A1 |
| 20200375499 | Hansen et al. | Dec 2020 | A1 |
| 20200375782 | Hansen et al. | Dec 2020 | A1 |
| 20200375783 | Hansen et al. | Dec 2020 | A1 |
| 20200375784 | Hansen et al. | Dec 2020 | A1 |
| 20200375785 | Hansen et al. | Dec 2020 | A1 |
| 20200375786 | Hansen et al. | Dec 2020 | A1 |
| 20200375809 | Sullivan et al. | Dec 2020 | A1 |
| 20200383637 | Hansen et al. | Dec 2020 | A1 |
| 20200383818 | Hansen et al. | Dec 2020 | A1 |
| 20200383819 | Sletten et al. | Dec 2020 | A1 |
| 20200383820 | Hansen et al. | Dec 2020 | A1 |
| 20200383821 | Hansen et al. | Dec 2020 | A1 |
| 20200390587 | Svanegaard et al. | Dec 2020 | A1 |
| 20200390588 | Hansen et al. | Dec 2020 | A1 |
| 20200390589 | Hansen et al. | Dec 2020 | A1 |
| 20200395120 | Svanegaard et al. | Dec 2020 | A1 |
| 20200395610 | Ono et al. | Dec 2020 | A1 |
| 20200405228 | Svanegaard et al. | Dec 2020 | A1 |
| 20200405229 | Svanegaard et al. | Dec 2020 | A1 |
| 20200405230 | Svanegaard et al. | Dec 2020 | A1 |
| 20210000414 | Svanegaard et al. | Jan 2021 | A1 |
| 20210000633 | Hansen et al. | Jan 2021 | A1 |
| 20210000634 | Svanegaard et al. | Jan 2021 | A1 |
| 20210000635 | Hansen et al. | Jan 2021 | A1 |
| 20210000636 | Hansen et al. | Jan 2021 | A1 |
| 20210007663 | Svanegaard et al. | Jan 2021 | A1 |
| 20210007881 | Svanegaard et al. | Jan 2021 | A1 |
| 20210015653 | Hansen et al. | Jan 2021 | A1 |
| 20210015654 | Hansen et al. | Jan 2021 | A1 |
| 20210022683 | Faarbaek et al. | Jan 2021 | A1 |
| 20210038424 | Svanegaard et al. | Feb 2021 | A1 |
| 20210059603 | Svanegaard et al. | Mar 2021 | A1 |
| 20210085511 | Hansen et al. | Mar 2021 | A1 |
| 20210085512 | Hansen et al. | Mar 2021 | A1 |
| 20210100533 | Seres et al. | Apr 2021 | A1 |
| 20210128364 | Cole et al. | May 2021 | A1 |
| 20210177642 | Andersen et al. | Jun 2021 | A1 |
| 20210212855 | Hansen et al. | Jul 2021 | A1 |
| 20210228194 | Mayberg | Jul 2021 | A1 |
| 20210338471 | Nolan et al. | Nov 2021 | A1 |
| 20210361464 | Larsen et al. | Nov 2021 | A1 |
| 20210361465 | Hansen et al. | Nov 2021 | A1 |
| 20210361466 | Hansen et al. | Nov 2021 | A1 |
| 20210361467 | Hansen et al. | Nov 2021 | A1 |
| 20210369197 | Hansen et al. | Dec 2021 | A1 |
| 20210369488 | Hansen et al. | Dec 2021 | A1 |
| 20210369489 | Hansen et al. | Dec 2021 | A1 |
| 20210369490 | Hansen et al. | Dec 2021 | A1 |
| 20210370217 | Kirschman | Dec 2021 | A1 |
| 20210386368 | Carlsson et al. | Dec 2021 | A1 |
| 20220000652 | Thirstrup et al. | Jan 2022 | A1 |
| 20220031227 | Cho et al. | Feb 2022 | A1 |
| 20220031495 | Seres et al. | Feb 2022 | A1 |
| 20220079802 | Hansen | Mar 2022 | A1 |
| 20220079803 | Windeballe et al. | Mar 2022 | A1 |
| 20220087851 | Stroebech | Mar 2022 | A1 |
| 20220110585 | Andersen | Apr 2022 | A1 |
| 20220117771 | Fearn et al. | Apr 2022 | A1 |
| 20220142807 | Tofte | May 2022 | A1 |
| 20220192860 | Hansen et al. | Jun 2022 | A1 |
| 20220241104 | Knoedler | Aug 2022 | A1 |
| 20220241105 | Hansen et al. | Aug 2022 | A1 |
| 20220265458 | Carlsson et al. | Aug 2022 | A1 |
| 20230059470 | Hansen et al. | Feb 2023 | A1 |
| 20230064734 | Hansen et al. | Mar 2023 | A1 |
| 20230105402 | Hansen et al. | Apr 2023 | A1 |
| 20230117727 | Hansen et al. | Apr 2023 | A1 |
| 20230118594 | Speiermann et al. | Apr 2023 | A1 |
| 20230145670 | Seres et al. | May 2023 | A1 |
| 20230190509 | Hansen et al. | Jun 2023 | A1 |
| 20230210682 | Hansen et al. | Jul 2023 | A1 |
| 20230233147 | Hansen et al. | Jul 2023 | A1 |
| 20230329893 | Olsen et al. | Oct 2023 | A1 |
| 20230338005 | Barthe et al. | Oct 2023 | A1 |
| Number | Date | Country |
|---|---|---|
| 2540756 | Jan 2008 | CA |
| 3009449 | Sep 2019 | CA |
| 3002372 | Mar 2021 | CA |
| 2947016 | Feb 2023 | CA |
| 103269668 | Aug 2013 | CN |
| 203786580 | Aug 2014 | CN |
| 104902399 | Sep 2015 | CN |
| 104980878 | Oct 2015 | CN |
| 105588856 | May 2016 | CN |
| 206271160 | Jun 2017 | CN |
| 206450708 | Aug 2017 | CN |
| 105615896 | May 2019 | CN |
| 105359167 | Jun 2019 | CN |
| 3437950 | Apr 1985 | DE |
| 3836590 | May 1990 | DE |
| 19953062 | May 2000 | DE |
| 19900611 | Jul 2000 | DE |
| 102011014321 | Sep 2012 | DE |
| 102011076219 | Nov 2012 | DE |
| 0168967 | Jan 1986 | EP |
| 0373782 | Jun 1990 | EP |
| 0416397 | Mar 1991 | EP |
| 0850076 | Apr 2005 | EP |
| 1188157 | Dec 2005 | EP |
| 2108345 | Oct 2009 | EP |
| 2489561 | Aug 2012 | EP |
| 2654646 | Oct 2013 | EP |
| 2453851 | Oct 2014 | EP |
| 3064179 | Sep 2016 | EP |
| 3213727 | Sep 2017 | EP |
| 2219679 | Dec 1989 | GB |
| 2225951 | Jun 1990 | GB |
| 2343628 | May 2000 | GB |
| 2465742 | Jun 2010 | GB |
| 2542093 | Mar 2017 | GB |
| 04-074882 | Mar 1992 | JP |
| 06-152077 | May 1994 | JP |
| 09-010184 | Jan 1997 | JP |
| 11-128352 | May 1999 | JP |
| 2000-093448 | Apr 2000 | JP |
| 2001-087299 | Apr 2001 | JP |
| 2002-055074 | Feb 2002 | JP |
| 2002-224093 | Aug 2002 | JP |
| 2005-323981 | Nov 2005 | JP |
| 2007-319561 | Dec 2007 | JP |
| 2014-033745 | Feb 2014 | JP |
| 2014-054368 | Mar 2014 | JP |
| 2014-507182 | Mar 2014 | JP |
| 10-2012-0003987 | Jan 2012 | KR |
| 1003904 | Mar 1998 | NL |
| 2527155 | Aug 2014 | RU |
| 201201783 | Jan 2012 | TW |
| 9415562 | Jul 1994 | WO |
| 9710012 | Mar 1997 | WO |
| 9933037 | Jul 1999 | WO |
| 9936017 | Jul 1999 | WO |
| 0079497 | Dec 2000 | WO |
| 0113830 | Mar 2001 | WO |
| 0150996 | Jul 2001 | WO |
| 0252302 | Jul 2002 | WO |
| 0299765 | Dec 2002 | WO |
| 2005038693 | Apr 2005 | WO |
| 2005082271 | Sep 2005 | WO |
| 2006008866 | Jan 2006 | WO |
| 2006094513 | Sep 2006 | WO |
| 2007000168 | Jan 2007 | WO |
| 2007059774 | May 2007 | WO |
| 2007070266 | Jun 2007 | WO |
| 2007098762 | Sep 2007 | WO |
| 2007133555 | Nov 2007 | WO |
| 2007128038 | Nov 2007 | WO |
| 2007133555 | Nov 2007 | WO |
| 2008057884 | May 2008 | WO |
| 2009006900 | Jan 2009 | WO |
| 2009052496 | Apr 2009 | WO |
| 2009107011 | Sep 2009 | WO |
| 2009112912 | Sep 2009 | WO |
| 2011003421 | Jan 2011 | WO |
| 2011004165 | Jan 2011 | WO |
| 2011061540 | May 2011 | WO |
| 2011105701 | Sep 2011 | WO |
| 2011123018 | Oct 2011 | WO |
| 2011139499 | Nov 2011 | WO |
| 2011161254 | Dec 2011 | WO |
| 2012068386 | May 2012 | WO |
| 2012076022 | Jun 2012 | WO |
| 2012084987 | Jun 2012 | WO |
| 2013013197 | Jan 2013 | WO |
| 2013095231 | Jun 2013 | WO |
| 2014004207 | Jan 2014 | WO |
| 2014086369 | Jun 2014 | WO |
| 2015007284 | Jan 2015 | WO |
| 2015014774 | Feb 2015 | WO |
| 2015084462 | Jun 2015 | WO |
| 2015094064 | Jun 2015 | WO |
| 2015187366 | Dec 2015 | WO |
| 2016132738 | Aug 2016 | WO |
| 2016166731 | Oct 2016 | WO |
| 2016162038 | Oct 2016 | WO |
| 2016192738 | Dec 2016 | WO |
| 2017023794 | Feb 2017 | WO |
| 2017062042 | Apr 2017 | WO |
| 2017067558 | Apr 2017 | WO |
| 2017067560 | Apr 2017 | WO |
| 2017074505 | May 2017 | WO |
| 2017088153 | Jun 2017 | WO |
| 2017108109 | Jun 2017 | WO |
| 2017136696 | Aug 2017 | WO |
| 2017190752 | Nov 2017 | WO |
| 2018028756 | Feb 2018 | WO |
| 2019094635 | May 2019 | WO |
| 2019120432 | Jun 2019 | WO |
| 2019161859 | Aug 2019 | WO |
| 2019161860 | Aug 2019 | WO |
| 2019161863 | Aug 2019 | WO |
| 2019174693 | Sep 2019 | WO |
| 2019174695 | Sep 2019 | WO |
| 2019213623 | Nov 2019 | WO |
| 2020035121 | Feb 2020 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 20220110585 A1 | Apr 2022 | US |
