Patent Application
20240008894
Currently, clinicians determine which needle guides to use for establishing vascular access via needles when preparing therefor by way of ultrasound imaging; however, clinician-selected needle guides are not always the best needle guides for establishing vascular access in view of multifaceted considerations of applicable criteria.
Disclosed herein are systems and methods for automatically determining needle guides to use for establishing vascular access.
Disclosed herein is a system for automatic determination of a needle guide for establishing vascular access. The system includes, in some embodiments, an ultrasound probe, a console operably coupled to the ultrasound probe, and a display screen optionally integrated into the console. The console includes one or more processors and memory including instructions configured to instantiate one or more processes when executed by the one-or-more processors for the automatic determination of the needle guide in accordance with ultrasound-imaging data, historical data, or a combination thereof. The automatic determination of the needle guide uses at least logic, algorithms, machine learning including a machine-learning model trained with the historical data, artificial intelligence, or a combination thereof. The display screen is configured to display an ultrasound image including one or more blood vessels below a skin surface of a patient as well as the needle guide resulting from the automatic determination of the needle guide for establishing vascular access to the one-or-more blood vessels in the ultrasound image.
In some embodiments, the system is further configured for automatic selection of a blood vessel of the one-or-more blood vessels for establishing vascular access in accordance with the ultrasound-imaging data, the historical data, or a combination thereof. The automatic selection of the blood vessel uses at least the logic, the algorithms, the machine learning, the artificial intelligence, or a combination thereof.
In some embodiments, the machine learning, the artificial intelligence, or both perform image recognition using the ultrasound-imaging data for the automatic selection of the blood vessel.
In some embodiments, the automatic selection of the blood vessel is optimized within an image buffer or window via one or more blood vessel-selection algorithms.
In some embodiments, the machine learning, the artificial intelligence, or both analyze Doppler ultrasound-imaging data when available for the automatic selection of the blood vessel.
In some embodiments, the automatic determination of the needle guide is further in accordance with a location of the blood vessel resulting from the automatic selection of the blood vessel.
In some embodiments, the needle guide resulting from the automatic determination of the needle guide is further in accordance with blood-vessel size of the blood vessel resulting from the automatic selection of the blood vessel.
In some embodiments, the needle guide resulting from the automatic determination of the needle guide is from a group of possible needle guides that vary by angle of approach, depth at image intersection, compatible needle sizes, or a combination thereof.
In some embodiments, the needle guide resulting from the automatic determination of the needle guide is further in accordance with trigonometric calculations resulting from a trigonometric algorithm of the algorithms.
In some embodiments, the system is further configured for automatic determination of a vascular access device (“VAD”) from an inventory of available VADs in accordance with the ultrasound-imaging data. The automatic determination of the VAD using at least the logic, the algorithms, the machine learning, the artificial intelligence, or a combination thereof is in view of VAD occupancy of the blood vessel or VAD purchase length of the blood vessel for each VAD of the inventory of available VADs.
In some embodiments, the historical data includes clinician feedback entered into the condole on whether the needle guide resulting from the automatic determination of the needle guide was successful in establishing vascular access.
Also disclosed herein is a method of a system for automatic determination of a needle guide for establishing vascular access. The method includes, in some embodiments, an instantiating step, a needle-guide determining step, and a displaying step. The instantiating step includes instantiating one or more processes by executing instructions therefor stored in memory of a console of the system by one or more processors of the console. The needle-guide determining step includes automatically determining the needle guide in accordance with ultrasound-imaging data gathered by an ultrasound probe operably coupled to the console, historical data, or a combination thereof. The needle-guide determining step uses at least logic, algorithms, machine learning including a machine-learning model trained with the historical data, artificial intelligence, or a combination thereof. The displaying step includes displaying on a display screen optionally integrated into the console an ultrasound image including one or more blood vessels below a skin surface of a patient. The displaying step also includes displaying the needle guide resulting from needle-guide determining step for establishing vascular access to the one-or-more blood vessels in the ultrasound image.
In some embodiments, the method further includes a blood vessel-selecting step. The blood vessel-selecting step includes automatically selecting a blood vessel of the one-or-more blood vessels for establishing vascular access in accordance with the ultrasound-imaging data, the historical data, or a combination thereof. The blood vessel-selecting step uses at least the logic, the algorithms, the machine learning, the artificial intelligence, or a combination thereof.
In some embodiments, the method further includes an image-recognizing step. The image-recognizing step includes performing image recognition with the machine learning, the artificial intelligence, or both using the ultrasound-imaging data for the blood vessel-selecting step.
In some embodiments, the method further includes a blood-vessel selection-optimizing step. The blood-vessel selection-optimizing step includes optimizing the automatic selection of the blood vessel within an image buffer or window via one or more blood vessel-selection algorithms.
In some embodiments, the method further includes a Doppler-analyzing step. The Doppler-analyzing step includes analyzing Doppler ultrasound-imaging data with the machine learning, the artificial intelligence, or both in the blood vessel-selecting step.
In some embodiments, the needle-guide determining step is further in accordance with a location of the blood vessel resulting from the blood vessel-selecting step.
In some embodiments, the needle guide resulting from the needle-guide determining step is further in accordance with blood-vessel size of the blood vessel resulting from the blood vessel-selecting step.
In some embodiments, the needle guide resulting from the needle-guide determining step is from a group of possible needle guides that vary by angle of approach, depth at image intersection, compatible needle sizes, or a combination thereof.
In some embodiments, the needle guide resulting from the needle-guide determining step is further in accordance with trigonometric calculations resulting from a trigonometric algorithm of the algorithms.
In some embodiments, the method further includes a VAD-determining step. The VAD-determining step includes automatically determining a VAD from an inventory of available VADs in accordance with the ultrasound-imaging data, the automatic determination of the VAD using at least the logic, the algorithms, the machine learning, the artificial intelligence, or a combination thereof in view of VAD occupancy of the blood vessel or VAD purchase length of the blood vessel for each VAD of the inventory of available VADs.
In some embodiments, the historical data includes clinician feedback entered into the condole on whether the needle guide resulting from the needle guide-determining step was successful in establishing vascular access.
These and other features of the concepts provided herein will become more apparent to those of skill in the art in view of the accompanying drawings and following description, which describe particular embodiments of such concepts in greater detail.
Before some particular embodiments are disclosed in greater detail, it should be understood that the particular embodiments disclosed herein do not limit the scope of the concepts provided herein. It should also be understood that a particular embodiment disclosed herein can have features that can be readily separated from the particular embodiment and optionally combined with or substituted for features of any of a number of other embodiments disclosed herein.
Regarding terms used herein, it should also be understood the terms are for the purpose of describing some particular embodiments, and the terms do not limit the scope of the concepts provided herein. Ordinal numbers (e.g., first, second, third, etc.) are generally used to distinguish or identify different features or steps in a group of features or steps, and do not supply a serial or numerical limitation. For example, “first,” “second,” and “third” features or steps need not necessarily appear in that order, and the particular embodiments including such features or steps need not necessarily be limited to the three features or steps. In addition, any of the foregoing features or steps can, in turn, further include one or more features or steps unless indicated otherwise. Labels such as “left,” “right,” “top,” “bottom,” “front,” “back,” and the like are used for convenience and are not intended to imply, for example, any particular fixed location, orientation, or direction. Instead, such labels are used to reflect, for example, relative location, orientation, or directions. Singular forms of “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
With respect to “logic,” logic refers to hardware, software, or firmware configured to perform one or more functions. As hardware, logic can refer to circuitry having data-processing or storage functionality. Examples of such circuitry include, but are not limited to, a hardware processor (e.g., a microprocessor, one or more processor cores, a digital-signal processor, a programmable gate array [“PGA”], a microcontroller, an application specific integrated circuit [“ASIC”], etc.), semiconductor memory, or the like. As software, logic can refer to one or more processes, one or more instances, Application Programming Interface(s) (API), subroutine(s), function(s), applet(s), servlet(s), routine(s), source code, object code, shared or dynamic link libraries (dll), or even one or more instructions. Such software can be stored in any type of a suitable non-transitory storage medium or transitory storage medium (e.g., electrical signals, optical signals, acoustical signals, or some other form of propagated signals). Examples of a non-transitory storage medium include, but are not limited to, a programmable circuit; a non-persistent storage medium such as volatile memory (e.g., any type of random-access memory [“RAM”]); a persistent storage medium such as non-volatile memory (e.g., read-only memory [“ROM”], power-backed RAM, flash memory, phase-change memory, etc.), a solid-state drive, a hard-disk drive, an optical-disc drive, or a portable memory device. As firmware, logic can be stored in persistent storage.
As used herein, a “vascular access device” can be a medical device for vascular access including, but not limited to, a catheter such as a peripherally inserted central catheter (“PICC”), a central venous catheter (“CVC”), a midline catheter, an intravenous line such as a peripheral intravenous line (“Ply”), or the like.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art.
Again, clinicians currently determine which needle guides to use for establishing vascular access via needles when preparing therefor by way of ultrasound imaging; however, clinician-selected needle guides are not always the best needle guides for establishing vascular access in view of multifaceted considerations of applicable criteria.
Disclosed herein are systems and methods for automatically determining needle guides to use for establishing vascular access.
As shown, the system 100 can include a console 104 and an ultrasound probe 106 configured to operably couple with each other. For example, the console 104 and the ultrasound probe 106 can be coupled together via wire or wirelessly through communications modules such as the communications module 108 of the console 104 shown in
The console 104 can include one or more processors 110 and memory 112. Optionally, the console 104 can further include a display screen 114 (e.g., touch screen).
The memory 112 can include random-access memory (“RAM”) or non-volatile memory (e.g., electrically erasable programmable read-only memory [“EEPROM”]), and the one-or-more processors 110 and the memory 112 of the console 104 can be configured to control various functions of the system 100, as well as executing various operations (e.g., processing electrical signals from the ultrasonic transducers of the ultrasound probe 106 into ultrasound images) during operation of the system 100 in accordance with executable instructions 116 therefor stored in the memory 112 for execution by the one-or-more processors 110. Indeed, the instructions 116 can be configured to instantiate one or more processes when executed by the one-or-more processors 110 for the automatic determination of the needle guide 102 in accordance with ultrasound-imaging data 118, historical data 120, or a combination thereof stored, at least temporarily (e.g., prior to a procedure), in a data store 122. However, the one-or-more processes are not limited to automatic determinations of needle guides. In an example, the one-or-more processes can also include automatic selection of a blood vessel of one or more blood vessels for establishing vascular access in accordance with the ultrasound-imaging data 118, the historical data 120, or a combination thereof. In another example, the one-or-more processes can also include automatic determination of a VAD from an inventory of available VADs in accordance with at least the ultrasound-imaging data 118, particularly in view of VAD occupancy of the blood vessel or VAD purchase length of the blood vessel for each VAD of the inventory of available VADs. Such automatic determinations can use at least logic 124, algorithms 126, machine learning 128 including one or more machine-learning models (“MLMs”) 130 trained with the historical data 120 for continuously improving needle-guide determinations, artificial intelligence 132 (e.g., an artificial neural network [“ANN”]), or a combination thereof.
As to the automatic selection of the blood vessel of the one-or-more blood vessels for establishing vascular access, the automatic selection of the blood vessel can be, again, in accordance with the ultrasound-imaging data 118, the historical data 120, or a combination thereof. Indeed, the machine learning 128, the artificial intelligence 132, or both can perform image recognition using at least the ultrasound-imaging data 118 for the automatic selection of the blood vessel, which can include a determination of blood-vessel size and location including depth of the blood vessel (see
As to the automatic determination of the needle guide 102 for establishing vascular access, the automatic determination of the needle guide 102 can be, again, in accordance with the ultrasound-imaging data 118, the historical data 120, or a combination thereof. Indeed, the machine learning 128, the artificial intelligence 132, or both can automatically determine the needle guide 102 in accordance with the blood-vessel size of the blood vessel resulting from the automatic selection of the blood vessel, location of the blood vessel resulting from the automatic selection of the blood vessel, which can include trigonometric calculations resulting from a trigonometric algorithm of the algorithms 126. Notably, the needle guide 102 resulting from the automatic determination of the needle guide 102 can be from a group of possible needle guides that vary by angle of approach, depth at image intersection, compatible needle sizes, or a combination thereof.
The machine learning 128 can include the one-or-more MLMs 130 and MLM-training logic 134 as shown in
The display screen 114 can be integrated into the console 104, as shown, or the display screen 114 can be part of a standalone monitor configured to operably couple with the console 104. The display screen 114 can be configured to display an ultrasound image including one or more blood vessels below a skin surface of a patient, for example, as alluded to in
While not shown, the console 104 can further include a power connection configured to enable an operable connection to an external power supply. An internal power supply (e.g., a battery) can also be employed either with or exclusive of the external power supply. Power management circuitry of the console 104 can regulate power use and distribution.
The ultrasound probe 106 can include a probe head 138 housing an array of ultrasonic transducers, wherein the ultrasonic transducers are piezoelectric ultrasonic transducers or capacitive micromachined ultrasonic transducers (“CMUTs”). As shown in
Methods can include at least the portion of the method of the system 100 shown in
The instantiating step can include instantiating the one-or-more processes set forth above by executing the instructions 116 therefor stored in the memory 112 of the console 104 of the system 100 by the one-or-more processors 110 of the console 104.
The blood vessel-selecting step can include automatically selecting a blood vessel of one or more blood vessels for establishing vascular access in accordance with the ultrasound-imaging data 118, the historical data 120, or a combination thereof. As set forth above, the blood vessel-selecting step can use at least the logic 124, the algorithms 126, the machine learning 128, the artificial intelligence 132, or a combination thereof.
The image-recognizing step can include performing image recognition with the machine learning 128, the artificial intelligence 132, or both using the ultrasound-imaging data 118 for blood vessel-selecting step.
The Doppler-analyzing step can include analyzing Doppler ultrasound-imaging data (e.g., a subset of the ultrasound-imaging data 118) with the machine learning 128, the artificial intelligence 132, or both in the blood vessel-selecting step.
The blood-vessel selection-optimizing step can include optimizing the automatic selection of the blood vessel within an image buffer or window via one or more blood vessel-selection algorithms of the algorithms 126.
The needle-guide determining step can include automatically determining the needle guide 102 in accordance with the ultrasound-imaging data 118 gathered by the ultrasound probe 106 operably coupled to the console 104, the historical data 120, or a combination thereof, which, notably, can include blood-vessel size and location including depth of the blood vessel from the blood vessel-selecting step. As set forth above, the needle-guide determining step can use at least the logic 124, the algorithms 126, the machine learning 128, the artificial intelligence 132, or a combination thereof. The needle guide 102 resulting from the needle-guide determining step can be from a group of possible needle guides that vary by angle of approach, depth at image intersection, compatible needle sizes, or a combination thereof.
The VAD-determining step can include automatically determining a VAD from an inventory of available VADs in accordance with the ultrasound-imaging data 118, the automatic determination of the VAD using at least the logic 124, the algorithms 126, the machine learning 128, the artificial intelligence 132, or a combination thereof in view of VAD occupancy of the blood vessel or VAD purchase length of the blood vessel for each VAD of the inventory of available VADs.
The displaying step can include displaying on the display screen 114 optionally integrated into the console 104 an ultrasound image including the one-or-more blood vessels below a skin surface of a patient. The displaying step can also include displaying the needle guide 102 resulting from the needle-guide determining step for establishing vascular access to the one-or-more blood vessels in the ultrasound image. Such displaying on the display screen 114 is illustrated in
While some particular embodiments have been disclosed herein, and while the particular embodiments have been disclosed in some detail, it is not the intention for the particular embodiments to limit the scope of the concepts provided herein. Additional adaptations or modifications can appear to those of ordinary skill in the art, and, in broader aspects, these adaptations or modifications are encompassed as well. Accordingly, departures may be made from the particular embodiments disclosed herein without departing from the scope of the concepts provided herein.
