Patent Application
20250099023
The present invention relates to dermatological diagnosis methods and apparatus and, more specifically, for a non-invasive method and apparatus for assisting in making a dermatological diagnosis.
Physicians, such as dermatologists, use a variety of tools to diagnose suspect dermatological phenomena, such as masses, lesions and tumors. Usually, a visual inspection of a suspect phenomenon is performed. Based on such an inspection, a physician might rule out certain conditions like melanoma when the suspect phenomenon appears as an ordinary mole in shape and color. However, some melanomas can mimic a mole in shape, symmetry and color, which might result in a misleading diagnosis.
Generally, a common mole resides on the surface of the epidermis. A melanoma, on the other hand, often extends through the epidermis into the dermis, sometimes more than 4 mm below the surface of the skin.
Current diagnostic methods for melanoma include a visual examination and a physician review of a patient's health history. A biopsy is taken when the visual examination and history indicate a higher risk. However, a physician might be reluctant to take a biopsy of what appears to be a common mole for reasons of cost and patient discomfort.
Therefore, there is a need for an inexpensive non-invasive tool to assist a physician in diagnosing melanoma and other dermatological phenomena.
The disadvantages of the prior art are overcome by the present invention which, in one aspect, is an apparatus, method and system for assisting dermatological diagnosis. The system illuminates a dermatological phenomenon with a plurality of lights of different wavelengths and captures images of the phenomenon at each wavelength. Each wavelength penetrates skin to a different depth and, therefore, each image is of the phenomenon at a different depth. The resulting set of images is processed by an artificial intelligence system, such as a convolutional neural network, which is trained to generate probabilities of the phenomenon being classified as belonging to each of a set of different diagnoses.
In another aspect, the invention is an apparatus for assisting diagnosis of a dermatological phenomenon, that includes a variable wavelength light source. A camera captures images of the dermatological phenomenon. A computer controls the variable wavelength light source and the camera. The computer programmed to: cause the variable wavelength light source to illuminate the dermatological phenomenon with a plurality of different wavelengths each at a corresponding plurality of different times; capture an image of the dermatological phenomenon illuminated with each of the plurality of different wavelengths at each different time so as to generate a set of images; execute a neural network that has been trained with images of a plurality of dermatological phenomena types with the set of images so as to generate a probability that the set of images corresponds to a selected phenomenon type of the plurality of dermatological phenomena types; and display an indication of a probability that the dermatological phenomenon corresponds to at least one of dermatological phenomena types.
In yet another aspect, the invention is a method of assisting diagnosis of a dermatological phenomenon, in which the dermatological phenomenon is illuminated with a plurality of different wavelengths at a corresponding plurality of different times. An image of the dermatological phenomenon illuminated with each of the plurality of different wavelengths is captured at each different time so as to generate a set of images. A neural network is executed. The neural network has been trained with images of a plurality of dermatological phenomena types with the set of images, thereby generating a probability that the set of images corresponds to a selected phenomenon type of the plurality of dermatological phenomena types. An indication of a probability that the dermatological phenomenon corresponds to at least one of dermatological phenomena types is displayed.
These and other aspects of the invention will become apparent from the following description of the preferred embodiments taken in conjunction with the following drawings. As would be obvious to one skilled in the art, many variations and modifications of the invention may be effected without departing from the spirit and scope of the novel concepts of the disclosure.
A preferred embodiment of the invention is now described in detail. Referring to the drawings, like numbers indicate like parts throughout the views. Unless otherwise specifically indicated in the disclosure that follows, the drawings are not necessarily drawn to scale. The present disclosure should in no way be limited to the exemplary implementations and techniques illustrated in the drawings and described below. As used in the description herein and throughout the claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise: the meaning of “a,” “an,” and “the” includes plural reference, the meaning of “in” includes “in” and “on.” Also, as used herein, “global computer network” includes the Internet.
As shown in
A camera 120 (e.g., a CMOS camera, a scientific CMOS camera, a charge-coupled device camera, etc.) captures images of the phenomenon of interest 12 while illuminated at each of the different wavelengths. Each captured image 140 is of the phenomenon of interest 12 illuminated at a different wavelength, which includes image data of the phenomenon of interest 12 at a different depth. For example, illuminating the phenomenon of interest 12 with light of wavelength λ1 results in image 141; illuminating it with light of wavelength λ2 results in image 142; illuminating it with light of wavelength λ3 results in image 143; illuminating it with light of wavelength λ4 results in image 144; illuminating it with light of wavelength λ5 results in image 145; and illuminating it with light of wavelength λ6 results in image 146.
The computer 130 employs an artificial intelligence (AI) system, such as a convolutional neural network (CNN). The CNN can run on the computer (typically on a graphics processing unit) or can run on a remote mainframe via the global computer network. The CNN is trained with sets of images of various dermatological phenomena in which each set is labelled according to the type of phenomenon it represents. For example, the CNN can be trained with sets of images of basal cell carcinoma, sets of images of melanoma, sets of images of squamous cell carcinoma, sets of images of Merkel cell carcinoma, sets of images of atypical moles, sets of images of typical moles, etc. More sets of images of each type of phenomenon that are used to train the CNN will deliver a better result.
As shown in
For example, when a set of images from a patient is processed by the CNN, the CNN could return the following probabilities: p(basal cell carcinoma)=0.05; p(melanoma)=0.13; p(squamous cell carcinoma)=0.06; p(Merkel cell carcinoma)=0.03; p(atypical moles)=0.68; and p(typical moles)=0.05. These probabilities would indicate that the set of images conforms most closely to those of atypical moles and that the next-most likely candidate would be melanoma. Using this information, a clinician would concentrate his or her investigation by using tools for diagnosing atypical moles and melanoma.
This system uses image data from different depths, thereby using more information than what would be available through surface visual inspection alone. Such information could be highly valuable in assisting the clinician in distinguishing between a typical mole and cancerous mass that appears like a typical mole.
Although specific advantages have been enumerated above, various embodiments may include some, none, or all of the enumerated advantages. Other technical advantages may become readily apparent to one of ordinary skill in the art after review of the following figures and description. It is understood that, although exemplary embodiments are illustrated in the figures and described below, the principles of the present disclosure may be implemented using any number of techniques, whether currently known or not. Modifications, additions, or omissions may be made to the systems, apparatuses, and methods described herein without departing from the scope of the invention. The components of the systems and apparatuses may be integrated or separated. The operations of the systems and apparatuses disclosed herein may be performed by more, fewer, or other components and the methods described may include more, fewer, or other steps. Additionally, steps may be performed in any suitable order. As used in this document, “each” refers to each member of a set or each member of a subset of a set. It is intended that the claims and claim elements recited below do not invoke 35 U.S.C. § 112(f) unless the words “means for” or “step for” are explicitly used in the particular claim. The above described embodiments, while including the preferred embodiment and the best mode of the invention known to the inventor at the time of filing, are given as illustrative examples only. It will be readily appreciated that many deviations may be made from the specific embodiments disclosed in this specification without departing from the spirit and scope of the invention. Accordingly, the scope of the invention is to be determined by the claims below rather than being limited to the specifically described embodiments above.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US22/44588 | 9/23/2022 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63248468 | Sep 2021 | US |
