Graphical user interface for prescreened in-vivo studies

Abstract

A system for presenting prescreened in-vivo studies includes at least one processor and at least one memory storing instructions. The instructions, when executed by the processor(s), cause the system at least to: access prescreened in-vivo studies of at least a portion of gastrointestinal tracts (GIT) of patients, where each of the prescreened in-vivo studies includes respective in-vivo images; access, for each of the prescreened in-vivo studies, respective prescreening information generated prior to the respective in-vivo images being read by a healthcare professional, where the respective prescreening information includes information on whether one or more GIT conditions were indicated by image prescreening processing of the respective in-vivo images; and provide a graphical user interface configured to present a listing for each of the prescreened in-vivo studies, where the graphical user interface is configured to present, in each listing, one or more graphical indications corresponding to whether the GIT condition(s) were indicated.

Description

GRAPHICAL USER INTERFACE FOR PRESCREENED IN-VIVO STUDIESCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims the benefit of and priority to U.S. Provisional Application No. 63 / 525,457, filed July 7, 2023, which is hereby incorporated by reference herein in its entirety.TECHNICAL FIELD

[0002] The present disclosure relates to in-vivo studies and, more particularly, to graphical user interfaces for presenting prescreened in-vivo studies.BACKGROUND

[0003] Capsule endoscopy (CE) allows examining of a gastrointestinal tract (GIT) endoscopically. There are capsule endoscopy systems and methods that are aimed at examining a specific portion of the GIT, such as the small bowel (SB) or the colon. CE is a non-invasive procedure which does not require the patient to be admitted to a hospital, and the patient can continue most daily activities while the capsule is in his body.

[0004] For a typical CE procedure, the patient is referred to a procedure by a physician. The patient then arrives at a medical facility (e.g., a clinic or a hospital), to perform the procedure. The capsule, which is about the size of a multi-vitamin, is swallowed by the patient under the supervision of a healthcare professional (e.g., a nurse or a physician) at the medical facility and the patient is provided with a wearable device (e.g., a belt having a recorder in a pouch and a strap to be placed around the patient’s shoulder). The wearable device typically includes a storage device. The patient may be given guidance and / or instructions and then is released to his or her daily activities.

[0005] The capsule captures images as it travels naturally through the GIT. Images and additional data (e.g., metadata) are then transmitted to the recorder that is worn by the patient. The capsule is typically disposable and passes naturally with a bowel movement. The procedure data (e.g., the captured images or a portion of them and additional metadata) is stored in the storage device of the wearable device.

[0006] The procedure data is uploaded from the wearable device to a computing system, which has a software engine stored thereon. The procedure data is then processed by the software engine to generate a compiled study. Typically, the number of images in the proceduredata to be processed is of the order of tens of thousands, and the generated study typically includes thousands of images.

[0007] A reader (which may be the procedure supervising physician, a dedicated physician, or the referring physician) may access the study via a reader application. The reader then reviews the study, evaluates the procedure, and provides input via the reader application. Since the reader needs to review thousands of images, the reading time of a study may usually take between half an hour to an hour on average and the reading task may be tiresome. A report is then generated by the reader application based on the compiled study and the reader’s input. On average, it may take an hour to generate a report. The report may include, for example, images of interest (e.g., images which are identified as including pathologies) selected by the reader; evaluation or diagnosis of the patient’s medical condition based on the procedure’s data (i.e., the study), and / or recommendations for follow up and / or treatment provided by the reader. The report may then be forwarded to a referring physician. The referring physician may decide on a required follow up or treatment based on the report.SUMMARY

[0008] The present disclosure relates to graphical user interfaces for presenting prescreened in-vivo studies. To the extent consistent, any or all of the aspects, embodiments, and examples detailed herein may be used in conjunction with any or all of the other aspects or embodiments detailed herein.

[0009] In accordance with aspects of the present disclosure, a system for presenting prescreened in-vivo studies includes at least one processor and at least one memory storing instructions. The instructions, when executed by the at least one processor, cause the system at least to: access a plurality of prescreened in-vivo studies of at least a portion of gastrointestinal tracts (GIT) of patients, where each of the plurality of prescreened in-vivo studies includes respective in-vivo images; access, for each of the plurality of prescreened in-vivo studies, respective prescreening information generated prior to the respective in-vivo images being read by a healthcare professional, where the respective prescreening information includes information on whether one or more GIT conditions were indicated by image prescreening processing of the respective in-vivo images; and provide a graphical user interface configured to present a listing for each of the plurality of prescreened in-vivo studies, where the graphical user interface is configured to present, in each listing, one or more graphical indications corresponding to whether the one or more GIT conditions were indicated by the imageprescreening processing.

[0010] In various embodiments of the system, for each of the plurality of prescreened in- vivo studies, the image prescreening processing processes the respective in-vivo images prior to any healthcare professional reading the respective in-vivo images.

[0011] In various embodiments of the system, the image prescreening processing is performed by at least one machine learning model.

[0012] In various embodiments of the system, the one or more GIT conditions include bleeding, vascular disease, and inflammatory disease, and the one or more graphical indications include a graphical indication that bleeding was indicated, a graphical indication that vascular disease was indicated, and a graphical indication that inflammatory disease was indicated.

[0013] In various embodiments of the system, the image prescreening processing is performed by at least a convolutional neural network configured to detect the one or more GIT conditions in in-vivo images.

[0014] In various embodiments of the system, the instructions, when executed by the at least one processor, cause the system at least to: prioritize display of listings which have one or more graphical indications indicating that one or more GIT conditions were indicated; and display the listings that are prioritized for display before displaying listings that are not prioritized for display.

[0015] In various embodiments of the system, the graphical user interface includes control elements configured to sort the listings according to one of the one or more graphical indications selected by a user of the graphical user interface.

[0016] In accordance with aspects of the present disclosure, a method for presenting in- vivo studies includes: accessing a plurality of prescreened in-vivo studies of at least a portion of gastrointestinal tracts (GIT) of patients, where each of the plurality of prescreened in-vivo studies includes respective in-vivo images; accessing, for each of the plurality of prescreened in-vivo studies, respective prescreening information generated prior to the respective in-vivo images being read by a healthcare professional, where the respective prescreening information includes information on whether one or more GIT conditions were indicated by image prescreening processing of the respective in-vivo images; and providing a graphical user interface configured to present a listing for each of the plurality of prescreened in-vivo studies, where the graphical user interface is configured to present, in each listing, one or more graphical indications corresponding to whether the one or more GIT conditions were indicated by the image prescreening processing.

[0017] In various embodiments of the method, for each of the plurality of prescreened in- vivo studies, the image prescreening processing processes the respective in-vivo images prior to any healthcare professional reading the respective in-vivo images.

[0018] In various embodiments of the method, the image prescreening processing is performed by at least one machine learning model.

[0019] In various embodiments of the method, the one or more GIT conditions include bleeding, vascular disease, and inflammatory disease, and the one or more graphical indications include a graphical indication that bleeding was indicated, a graphical indication that vascular disease was indicated, and a graphical indication that inflammatory disease was indicated.

[0020] In various embodiments of the method, the image prescreening processing is performed by at least a convolutional neural network configured to detect the one or more GIT conditions in in-vivo images.

[0021] In various embodiments of the method, the method includes: prioritizing display of listings which have one or more graphical indications indicating that one or more GIT conditions were indicated; and displaying the listings that are prioritized for display before displaying listings that are not prioritized for display.

[0022] In various embodiments of the method, the graphical user interface includes control elements configured to sort the listings according to one of the one or more graphical indications selected by a user of the graphical user interface.

[0023] In accordance with aspects of the present disclosure, a processor-readable medium stores instructions which, when executed by at least one processor of a system, cause the system at least to: access a plurality of prescreened in-vivo studies of at least a portion of gastrointestinal tracts (GIT) of patients, where each of the plurality of prescreened in-vivo studies includes respective in-vivo images; access, for each of the plurality of prescreened in- vivo studies, respective prescreening information generated prior to the respective in-vivo images being read by a healthcare professional, where the respective prescreening information includes information on whether one or more GIT conditions were indicated by image prescreening processing of the respective in-vivo images; and provide a graphical user interface configured to present a listing for each of the plurality of prescreened in-vivo studies, where the graphical user interface is configured to present, in each listing, one or more graphical indications corresponding to whether the one or more GIT conditions were indicated by the image prescreening processing.

[0024] In various embodiments of the processor-readable medium, for each of the pluralityof prescreened in-vivo studies, the image prescreening processing processes the respective in- vivo images prior to any healthcare professional reading the respective in-vivo images.

[0025] In various embodiments of the processor-readable medium, the image prescreening processing is performed by at least one machine learning model.

[0026] In various embodiments of the processor-readable medium, the one or more GIT conditions include bleeding, vascular disease, and inflammatory disease, and the one or more graphical indications include a graphical indication that bleeding was indicated, a graphical indication that vascular disease was indicated, and a graphical indication that inflammatory disease was indicated.

[0027] In various embodiments of the processor-readable medium, the image prescreening processing is performed by at least a convolutional neural network configured to detect the one or more GIT conditions in in-vivo images.

[0028] In various embodiments of the processor-readable medium, the instructions, when executed by the at least one processor, causes the system at least to: prioritize display of listings which have one or more graphical indications indicating that one or more GIT conditions were indicated; and display the listings that are prioritized for display before displaying listings that are not prioritized for display.

[0029] In various embodiments of the processor-readable medium, the graphical user interface further includes control elements configured to sort the listings according to one of the one or more graphical indications selected by a user of the graphical user interface.

[0030] Further details and aspects of exemplary embodiments of the present disclosure are described in more detail below with reference to the appended figures.BRIEF DESCRIPTION OF THE DRAWINGS

[0031] A better understanding of the features and advantages of the disclosed technology will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the technology are utilized, and the accompanying drawings of which:

[0032] FIG. l is a diagram of a gastrointestinal tract (GIT);

[0033] FIG. 2 is a block diagram of an example of a system for analyzing medical images captured in-vivo via a Capsule Endoscopy (CE) procedure and for providing the images to a user system or device, in accordance with aspects of the disclosure;

[0034] FIG. 3 is a block diagram of an example of components of a computing system or of a user system, in accordance with aspects of the disclosure;

[0035] FIG. 4 is a diagram of an example of a graphical user interface for displaying listings of in-vivo studies, in accordance with aspects of the disclosure;

[0036] FIG. 5 is a flow diagram of an example of an operation for displaying listings of in- vivo studies, in accordance with aspects of the disclosure;

[0037] FIG. 6 is a diagram of an example of a graphical user interface for displaying information for an in-vivo study, in accordance with aspects of the disclosure;

[0038] FIG. 7 is a diagram of an example of a graphical user interface for displaying information for an indicated GIT condition, in accordance with aspects of the disclosure;

[0039] FIG. 8 is a diagram of an example of a graphical user interface for displaying an image indicated as showing a GIT condition, in accordance with aspects of the disclosure; and

[0040] FIG. 9 is a diagram of an example of a graphical user interface for displaying images indicated as showing GIT conditions, in accordance with aspects of the disclosure.DETAILED DESCRIPTION

[0041] The present disclosure relates to graphical user interfaces for presenting prescreened in-vivo studies. An in-vivo imaging device, such as a capsule endoscope, captures images of a gastrointestinal tract (“GIT”). The images are later reviewed by a healthcare professional (e.g., physician, specialist, imaging technician, or otherwise) to evaluate the health or condition of the GIT or of portions of the GIT. Because a healthcare professional can analyze an in-vivo study after the capsule endoscope procedure is completed, the healthcare professional may order a large number of capsule endoscope procedures and review them after the procedures are completed. Typically, a healthcare professional would review the in-vivo studies chronologically in the order they are completed. In case of a backlog, however, a significant amount of time may pass before certain studies are eventually reviewed by a healthcare professional. If a study shows that a patient has a condition, the backlog and the wait time until the study is reviewed may meaningfully increase risk to the patient.

[0042] Aspects of the present disclosure relate to a graphical user interface (GUI) for presenting prescreened in-vivo studies in a manner that allows a healthcare professional to prioritize reading of certain in-vivo studies. In aspects, the prescreening may be performed by image processing the in-vivo images in a study and may be performed prior to the in-vivo study being read by a healthcare professional. As explained in more detail below, the imageprocessing may be performed by a machine learning model, such as a convolutional neural network. The prescreening may indicate whether the in-vivo images reveal certain GIT health conditions (such as bleeding or colon polyps, among others), and the GUI may present graphical indications corresponding to those GIT health conditions. In this manner, a healthcare professional may better manage patient risk by prioritizing reading of in-vivo studies which have prescreening information indicating that certain GIT health conditions may be present.

[0043] In the following detailed description, specific details are set forth in order to provide a thorough understanding of the disclosure. However, it will be understood by those skilled in the art that the disclosure may be practiced without these specific details. In other instances, well-known methods, procedures, and components have not been described in detail so as not to obscure the present disclosure. Some features or elements described with respect to one system may be combined with features or elements described with respect to other systems. For the sake of clarity, discussion of same or similar features or elements may not be repeated.

[0044] To the extent consistent, any or all of the aspects, embodiments, and examples detailed herein may be used in conjunction with any or all of the other aspects or embodiments detailed herein.

[0045] Although the disclosure is not limited in this regard, discussions utilizing terms such as, for example, “processing,” “computing,” “calculating,” “determining,” “establishing,” “analyzing,” “checking,” or the like, may refer to operation(s) and / or process(es) of a processor, a computing platform, a computing system, or other electronic computing device, that manipulates and / or transforms data represented as physical (e.g., electronic) quantities within computing registers and / or memories into other data similarly represented as physical quantities within the computing registers and / or memories or other non-transitory information storage medium that may store instructions to perform operations and / or processes.

[0046] Although the disclosure is not limited in this regard, the terms “plurality” and “a plurality” as used herein may include, for example, “multiple” or “two or more.” The terms “plurality” or “a plurality” may be used throughout the specification to describe two or more components, devices, elements, units, parameters, or the like.

[0047] As used herein, the term “exemplary” means “an example” and is not intended to mean preferred. Unless explicitly stated, the methods described herein are not constrained to a particular order or sequence. Additionally, some of the described methods or elements thereof can occur or be performed simultaneously, at the same point in time, or concurrently.

[0048] Depending on the context, the term “GIT” may mean a portion of the gastrointestinal tract and / or the entirety of a gastrointestinal tract. Thus, disclosures relating to a GIT may apply to a portion of the GIT and / or the entirety of a GIT.

[0049] The terms “image” and “frame” may each refer to or include the other and may be used interchangeably in the present disclosure to refer to a single capture by an imaging device. For convenience, the term “image” may be used more frequently in the present disclosure, but it will be understood that references to an image shall apply to a frame as well. The term “in- vivo study” means and includes a compilation of medical images that are captured in-vivo by an imaging device for a single in-vivo procedure. An in-vivo study may include any number of such medical images for a single in-vivo procedure, which may be a capsule endoscopy procedure or may be another type of in-vivo procedure.

[0050] The present disclosure may refer to in-vivo images or studies that have an “indication” of one or more GIT conditions. Such descriptions do not mean and are not intended to mean that such images or studies definitely show the GIT condition(s). Additionally, the present disclosure may refer to in-vivo images or studies that have an “indication” of no GIT conditions. Such descriptions do not mean and are not intended to mean that such images or studies definitely do not show the GIT condition(s). Rather, such descriptions mean and are intended to mean that the images or studies merely have such indications.

[0051] The term “machine learning” means and includes any technique which analyzes existing data to learn a model between inputs and outputs in the existing data. The term “machine learning model” means and includes any implementation of the learned model, in software and / or hardware, that can receive new input data and that can predict / infer output data by applying the learned model to the new input data. Machine learning may include supervised learning and unsupervised learning, among other things. Examples of machine learning models include, without limitation, deep learning neural networks and support vector machines, among other things.

[0052] The following description refers to images captured by a capsule endoscopy device. However, it is intended and shall be understood that the following description applies to other manners of obtaining images of a GIT or portion of a GIT, as well.

[0053] Referring to FIG. 1, an illustration of a gastrointestinal tract (GIT) 100 is shown. The GIT 100 is an organ system within humans and animals. The GIT 100 generally includes a mouth 102 for taking in sustenance, salivary glands 104 for producing saliva, an esophagus 106 through which food passes aided by contractions, a stomach 108 to secret enzymes andstomach acid to aid in digesting food, a liver 110, a gall bladder 112, a pancreas 114, a small intestine / small bowel 116 (“SB”) for the absorption of nutrients, and a colon 40 (e.g., large intestine) for storing water and waste material as feces prior to defecation. The colon 40 generally includes an appendix 42, a rectum 48, and an anus 43. Food taken in through the mouth is digested by the GIT to take in nutrients and the remaining waste is expelled as feces through the anus 43.

[0054] The type of procedure performed may determine which portion of the GIT 100 is the portion of interest. Examples of types of procedures performed include, without limitation, a procedure aimed to specifically exhibit or check the small bowel, a procedure aimed to specifically exhibit or check the colon, a procedure aimed to specifically exhibit or check the colon and the small bowel, or a procedure to exhibit or check the entire GIT: esophagus, stomach, SB, and colon, among other possibilities.

[0055] FIG. 2 shows a block diagram of a system for analyzing medical images captured in-vivo via a capsule endoscopy (“CE”) procedure and providing the images to a user system or device. The system generally includes a capsule system 210 configured to capture images of the GIT and a computing system 230 (e.g., local system and / or cloud system) configured to process the captured images.

[0056] The capsule system 210 may include a swallowable CE imaging device 212 (e.g., a capsule) configured to capture images of the GIT as the CE imaging device 212 travels through the GIT. The images may be stored on the CE imaging device 212 and / or transmitted to a receiving device 214, typically via an antenna. In some capsule systems 210, the receiving device 214 may be located on the patient who swallowed the CE imaging device 212 and may, for example, take the form of a belt worn by the patient or a patch secured to the patient.

[0057] The capsule system 210 may be communicatively coupled with the computing system 230 and can communicate captured images to the computing system 230. The computing system 230 may process the received images using image processing technologies, machine learning technologies, and / or signal processing technologies, among other technologies. The computing system 230 may include local computing devices that are local to the patient and / or local to the patient’s treatment facility, a cloud computing platform that is provided by cloud services, or a combination of local computing devices and a cloud computing platform.

[0058] In the case where the computing system 230 includes a cloud computing platform, the images captured by the capsule system 210 may be transmitted to the cloud computingplatform. In various embodiments, the images can be transmitted by or via the receiving device 214 worn or carried by the patient. In various embodiments, the images can be transmitted via the patient’s smartphone or via any other device which is connected to the Internet and which may be coupled with the CE imaging device 212 or the receiving device 214.

[0059] The images processed by the computing system 230 and / or the processing results may be communicated to a user system or user device 250 for a healthcare professional to read. In various embodiments, the computing system 230 may host a downloadable application and may provide the downloadable application to the user system / device 250. The user system / device 250 may download, install, and execute the application. In such embodiments, the application executing on the user system / device 250 may communicate with the computing system 230 to obtain study images and study information and then display such images and information locally on the user system / device 250 in a graphical user interface (GUI). In various embodiments, the computing system 230 may provide a web service, and the user system / device 250 may access the web service using a web browser. In such embodiments, the computing system 230 provides the study images and study information to the user system / device 250 in a GUI as website content. Such examples are merely illustrative and do not limit how a computing system may provide study images and study information to a user system / device.

[0060] FIG. 3 shows a block diagram of example components of the computing system 230 of FIG. 2 and / or the user system / device 250 of FIG. 3. The system 300 includes a processor 305, an operating system 315, a memory 320, a communication device 322, a storage 330, input devices 335, and output devices 340. The communication device 322 of the system 300 may allow communications with other systems or devices via a wired network (e.g., Ethernet) and / or a wireless network (e.g., Wi-Fi, cellular network, etc.).

[0061] The processor 305 may be or may include one or more central processing units (CPU), graphics processing unit (GPU), controllers, microcontrollers, microprocessors, and / or other computational devices. The operating system 315 may be or may include any code segment designed and / or configured to perform tasks involving coordination, scheduling, arbitration, supervising, controlling or otherwise managing operation of system 300, for example, scheduling execution of programs. Memory 320 may be or may include, for example, a Random Access Memory (RAM), a read-only memory (ROM), a Dynamic RAM (DRAM), a Synchronous DRAM (SD-RAM), a double data rate (DDR) memory chip, a Flash memory, a volatile memory, a non-volatile memory, a cache memory, a buffer, a short term memory, along term memory, and / or other memory devices. The memory 320 stores executable code 325 that implements the data and operations of the present disclosure, which will be described later herein. Executable code 325 may be any executable code, e.g., an application, a program, a process, task, or script. Executable code 325 may be executed by the processor 305 possibly under control of operating system 315.

[0062] Storage 330 may be or may include, for example, a hard disk drive, a solid-state drive (SSD), a digital versatile disc (DVD), a universal serial bus (USB) device, and / or other removable and / or fixed device for storing electronic data. Instructions / code and data (e.g., images) may be stored in the storage 330 and may be loaded from the storage 330 into the memory 320, where it may be processed by processor 305.

[0063] Input devices 335 may include, for example, a mouse, a keyboard, a touch screen, and / or any other device that can receive an input. Output devices 340 may include one or more monitors, screens, displays, speakers, and / or any other device that can provide an output.

[0064] Other aspects of the system 300 and the capsule system (210, FIG. 2) are described in International Publication No. WO2020236683A1, entitled “Systems and Methods For Capsule Endoscopy Procedure,” which is hereby incorporated by reference in its entirety. Generally, the technology of the present disclosure may be utilized by capsule endoscopy systems or methods and may be presented in a user interface, such as the example user interfaces described in International Publication No. W02020079696, entitled “Systems and Methods for Generating and Displaying a Study of a Stream of In-Vivo Images,” which is hereby incorporated by reference herein in its entirety.

[0065] As mentioned above, aspects of the present disclosure relate to a GUI for presenting prescreened in-vivo studies. As described above, in various embodiments, an application may execute on a user system / device and may display study images and study information locally on the user system / device 250 in a GUI. In various embodiments, a user system / device may access a web service using a web browser, and the web service may provide the study images and study information to the user system / device in a GUI as website content. Such examples are merely illustrative, and other implementations are contemplated to be within the scope of the present disclosure.

[0066] FIG. 4 is a diagram of an example of a GUI. In accordance with aspects of the present disclosure, the GUI displays listings 411-416 of in-vivo studies that have been prescreened by image processing. Such image processing may be referred to herein as “image prescreening processing.” In accordance with aspects of the present disclosure, imageprescreening processing may be performed by a computing system, such as by the computing system 230 of FIG. 2. The image prescreening processing may be implemented using any image processing technique, including by machine learning models such as convolutional neural networks, among other image processing techniques. Persons skilled in the art will understand how to implement image processing techniques, machine learning models, and convolutional neural networks.

[0067] In accordance with aspects of the present disclosure, image prescreening processing may analyze in-vivo images in an in-vivo study to indicate whether one or more GIT conditions may be shown in the in-vivo images. As mentioned above, an “indication” that an in-vivo study shows one or more GIT conditions does not mean that the in-vivo study definitely shows the GIT condition(s). Although image processing techniques may have high accuracy, they may not be accurate 100% of the time. Accordingly, when an in-vivo study is indicated to have one or more GIT conditions, the in-vivo study may or may not actually have such GIT condition(s). Also, an “indication” that an in-vivo study does not show any GIT conditions does not mean that the in-vivo study definitely does not show any GIT conditions. When an in-vivo study is indicated to have no GIT conditions, the in-vivo study may or may not actually have GIT conditions.

[0068] Examples of various GIT conditions are shown in the GUI of FIG. 4, which includes a graphical indication of blood 421, a graphical indication of vascular disease 423, and a graphical indication of inflammatory disease 424. An example of image processing that indicates bleeding is disclosed in U.S. Patent No. 9,364,139, which is hereby incorporated by reference herein in its entirety. An example of image processing that indicates vascular disease or inflammatory disease, among other diseases, is disclosed in U.S. Patent No. 11,478,125, which is hereby incorporated by reference herein in its entirety. These examples are merely illustrative, and other GIT conditions and graphical indications are contemplated to be within the scope of the present disclosure.

[0069] In various situations, image prescreening processing may indicate that an in-vivo study does not show any GIT conditions. In such situations, the GUI may display a graphical indication of no conditions 425.

[0070] In various embodiments, image prescreening processing may determine that a study was incomplete (e.g., due to a capsule endoscope becoming stuck in a patient or malfunctioning, among other possibilities). An example of image processing that indicates the completeness of a capsule endoscopy procedure is disclosed in U.S. Patent ApplicationPublication No. 2021 / 0345865, which is hereby incorporated by reference herein in its entirety. This determination may be indicated in the GUI by a graphical indication of an incomplete study 422.

[0071] The GUI may display a listing for each in-vivo study that was prescreened by image prescreening processing. The illustrated GUI shows six listings 411-416 of prescreened in- vivo studies. Each listing 411-416 includes a procedure date, a patient name, and a patient ID. Generally, a listing may display any relevant information. Other information not shown in FIG. 4 may be displayed and is contemplated to be within the scope of the present disclosure. Additionally, each listing 411-416 may display one or more graphical indications of GIT conditions (e.g., 421, 423, 424), may display a graphical indication of an incomplete study (e.g., 422), or may display a graphical indication of no GIT conditions (e.g., 425). The illustrated graphical indications are merely examples, and other graphical indications are contemplated to be within the scope of the present disclosure.

[0072] In accordance with aspects of the present disclosure, a listing for a prescreened in- vivo study may display multiple graphical indications of GIT conditions. In the example of FIG. 4, the listing 413 includes a graphical indication of blood and a graphical indication of vascular disease, and the listing 414 includes a graphical indication of inflammatory disease and a graphical indication of vascular disease. In various embodiments, a listing may display a graphical indication for every GIT condition that is indicated by image prescreening processing. In various embodiments, a listing may display graphical indications for only a configurable number of GIT conditions, such as two graphical indications or three graphical indications, for example, or another number.

[0073] In the GUI, each listing may display the number of observations 430 of GIT conditions, as observed by the image prescreening processing. In various embodiments, each observation may correspond to a separate image that shows the GIT condition. In various embodiments, each observation may correspond to a separate sequence of images that shows the GIT condition. Such and other ways for determining observations are contemplated to be within the scope of the present disclosure. Where a listing includes graphical indications for multiple GIT conditions, the number of observations for each GIT condition may be displayed.

[0074] As shown in FIG. 4, for listing with multiple graphical indications of GIT conditions, the graphical indications of GIT conditions may be displayed in order of highest number of observations to lowest number of observations. In various embodiments, multiple graphical indications of GIT conditions may be displayed in another order, such as in order ofseverity of the GIT condition (not shown), or in another order. Such and other embodiments of display order are contemplated to be within the scope of the present disclosure.

[0075] In aspects of the present disclosure, listings may be displayed in a particular order based on, for example, number of indicated GIT conditions and / or number of observations for indicated GIT conditions. In various embodiments, listings having greater number of observations for indicated GIT conditions may be displayed higher in the GUI than listings having lower number of observations for indicated GIT conditions. In various embodiments, listings have greater number of GIT conditions may be displayed higher in the GUI than listings have lower number of GIT conditions. A combination of such embodiments may be implemented. Such and other embodiments are contemplated to be within the scope of the present disclosure.

[0076] In aspects of the present disclosure, the listings may be sortable by the GUI (not shown). A user may, for example, sort the listings by any displayed information element, such as by procedure date, by patient name, by patient ID, by number of observations, and / or by indicated GIT condition. Other information elements not shown in FIG. 4 may be displayed in listings and may be used for sorting the listings. In various embodiments, the GUI and / or a user may implement a customized ordering of listings according to customizable criteria. For example, the criteria may specify that studies having a certain age (e.g., completed more than thirty days ago) are to be displayed first in the GUI. As another example, the criteria may specify numerical criteria for number of observations for certain GIT conditions, e.g., display listing earlier if indicated GIT condition is blood and number of observations is greater than thirty (30). In yet another example, the criteria may specify that listings indicated as incomplete study should be displayed earlier than certain other listings. Multiple criteria may be specified to implement a sorting of the listings. The examples are merely illustrative, and other criteria and ordering of listings are contemplated to be within the scope of the present disclosure.

[0077] FIG. 4 is merely illustrative. In various embodiments, other information not shown in FIG. 4 may be displayed by a GUI. In various embodiments, certain information shown in FIG. 4 may not be displayed by a GUI. In various embodiments, information may be displayed by a GUI in a different configuration. Such and other variations are contemplated to be within the scope of the present disclosure.

[0078] FIG. 5 is a flow diagram of an example of an operation for displaying listings of in- vivo studies. As explained below, the operation may be performed in a computing system, suchas computing system 230 of FIG. 2, or may be performed in a user system / device, such as user system / device 250 of FIG. 2.

[0079] Prior to block 510, in-vivo studies have already been processed by image prescreening processing.

[0080] At block 510, the operation involves accessing a plurality of prescreened in-vivo studies of at least a portion of gastrointestinal tracts (GIT) of patients. Each of the prescreened in-vivo studies includes in-vivo images. If the operation of FIG. 5 is performed by a computing system, the computing system may access the prescreened in-vivo studies from a memory or storage of the computing system, such as those shown in FIG. 3. If the operation of FIG. 5 is performed by a user system or device, the user system or device may access the prescreened in-vivo studies from a computing system via a communication network and / or the Internet.

[0081] At block 520, the operation involves accessing, for each of the plurality of prescreened in-vivo studies, prescreening information generated prior to the respective in-vivo images being read by a healthcare professional. The prescreening information includes information on whether one or more GIT conditions were indicated by the image prescreening processing of the in-vivo images in the in-vivo study. The image prescreening processing may be performed by a computing system in the manner described above with respect to FIG. 4. If the operation of FIG. 5 is performed by a computing system, the computing system may access the prescreening information from a memory or storage of the computing system, such as those shown in FIG. 3. If the operation of FIG. 5 is performed by a user system or device, the user system or device may access the prescreening information from a computing system via a communication network and / or the Internet.

[0082] At block 530, the operation involves providing a graphical user interface configured to present a listing for each of the plurality of prescreened in-vivo studies. The graphical user interface is configured to present, in each listing, one or more graphical indications corresponding to whether the one or more GIT conditions were indicated by the image prescreening processing. The GUI may be a GUI as shown and described in connection with FIG. 4. If the operation of FIG. 5 is performed by a computing system, the computing system may provide the GUI in a downloadable application and / or provide the GUI via a web service, among other possibilities. In the case of a downloadable application, the computing system may provide the GUI in the downloadable application prior to performing blocks 510 and 520. If the operation of FIG. 5 is performed by a user system or device, the user system or device may provide the GUI by executing a downloaded application to display the GUI and / or providethe GUI by accessing a web service to display the GUI as web content, among other possibilities.

[0083] FIG. 5 is merely illustrative. In various embodiments, the operation may involve other aspects not shown in FIG. 5. In various embodiments, the operation may not involve certain aspects shown in FIG. 5. In various embodiments, the aspects may be implemented in a different order than that shown in FIG. 5. Such and other variations are contemplated to be within the scope of the present disclosure.

[0084] Accordingly, FIG. 4 provided an example of a GUI and FIG. 5 provided an example of an operation for providing a GUI. FIGS. 6-9 show examples of a GUI for displaying information of an in-vivo study and will now be described.

[0085] FIG. 6 is a diagram of an example of a graphical user interface for displaying information for an in-vivo study. The GUI of FIG. 6 may be displayed, for example, in response to a user selecting a listing from the GUI of FIG. 4. In FIG. 6, the GUI displays user interface elements (e.g., a pulldown interface) for each indicated GIT condition, such as blood, vascular disease, inflammatory disease, and / or protruding, among other possible conditions. The GUI may display the number of observations of the indicated GIT conditions (as observed by image prescreening processing) in the user interface elements. As mentioned above, each observation may correspond to a separate image that shows the GIT condition, or may correspond to a separate sequence of images that shows the GIT condition, or may correspond to another instance of a GIT condition.

[0086] FIG. 7 is a diagram of a graphical user interface after a user selects the user interface element for blood in FIG. 6. The user interface element 710 is expanded and displays the observations of blood in the in-vivo images for the in-vivo study. If each observation corresponds to a single image, the expanded user interface element 710 may display such images. If each observation corresponds to a sequence of images, the expanded user interface element 710 may display a representative image from each sequence of images. The expanded user interface element 710 may allow a user to scroll or otherwise navigate to view each of the observations.

[0087] FIG. 8 is a diagram of a graphical user interface after a user selects an image in the expanded user interface element in FIG. 7. A user selects an image 812, and the GUI displays an enlarged version 820 of the selected image 812 in a portion of the GUI separate from the user interface elements. In various embodiments, the GUI may display a resized user interface element 810 that is a different size from the expanded user interface element 710 shown inFIG. 7. The GUI may optionally show a bar 830 that maps to the in-vivo images of the selected in-vivo study and show a marker 832 in the bar 830 corresponding to the location of the selected image 812. The GUI may optionally display navigation user interface elements 840 for navigating to other in-vivo images. In various embodiments, the navigation user interface elements 840 allows a user to navigate to other observations of the GIT condition shown in the user interface element 810. In various embodiments, the navigation user interface elements 840 allows a user to navigate to other in-vivo images adjacent to the selected image 812 or to navigate to any other in-vivo image in the in-vivo study.

[0088] FIG. 9 is a diagram of another example of a graphical user interface for displaying in-vivo images of an in-vivo study. The GUI includes a user interface element 910 that displays observations of indicated GIT conditions. In contrast to FIG. 7 and FIG. 8, the user interface element 910 displays all observations of all indicated GIT conditions and may allow a user to scroll through the observations. The GUI also includes a bar 930 that maps to the in-vivo images of the in-vivo study and includes markers 940 that show the locations of the observations with respect to the bar 930. The user may either select an observation in the user interface element 910 (e.g., select observation 912) or select an observation using the markers 940 (e.g., select observation 942). The GUI displays an enlarged version 920 of the selected observation. In aspects of the present disclosure, the GUI allows a user to annotate the in-vivo image of an observation, such as by adding a shape 922 around a condition, as shown in FIG. 9. Other types of annotations (e.g., text annotation, voice annotation, arrows, other graphical shapes, etc.) are contemplated to be within the scope of the present disclosure. The GUI may include buttons 950 for adding an in-vivo image (with or without annotations) to an in-vivo report, which when completed may be a report that can be sent to the patient and / or to a referring physician, among other people.

[0089] The examples of FIGS. 6-9 are illustrative of a GUI, and variations are contemplated to be within the scope of the present disclosure. For example, various aspects shown or described in FIGS. 6-9 may be combined in various ways. In various embodiments, a GUI may include other aspects not shown in FIGS. 6-9. In various embodiments, a GUI may not include every aspect shown in one or more of FIGS. 6-9. In various embodiments, a GUI may be arranged or configured differently from those shown in FIGS. 6-9. Such and other embodiments are contemplated to be within the scope of the present disclosure.

[0090] The embodiments disclosed herein are examples of the disclosure and may be embodied in various forms. For instance, although certain embodiments herein are describedas separate embodiments, each of the embodiments herein may be combined with one or more of the other embodiments herein. Specific structural and functional details disclosed herein are not to be interpreted as limiting, but as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure. Like reference numerals may refer to similar or identical elements throughout the description of the figures.

[0091] The phrases “in an embodiment,” “in embodiments,” “in various embodiments,” “in some embodiments,” or “in other embodiments” may each refer to one or more of the same or different embodiments in accordance with the present disclosure. A phrase in the form “A or B” means “(A), (B), or (A and B).” A phrase in the form “at least one of A, B, or C” means “(A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).”

[0092] The systems, devices, and / or servers described herein may utilize one or more processors to receive various information and transform the received information to generate an output. The processors may include any type of computing device, computational circuit, or any type of controller or processing circuit capable of executing a series of instructions that are stored in a memory. The processor may include multiple processors and / or multicore central processing units (CPUs) and may include any type of device, such as a microprocessor, graphics processing unit (GPU), digital signal processor, microcontroller, programmable logic device (PLD), field programmable gate array (FPGA), or the like. The processor may also include a memory to store data and / or instructions that, when executed by the one or more processors, causes the one or more processors to perform one or more methods and / or algorithms.

[0093] Any of the herein described methods, programs, algorithms, or codes may be converted to, or expressed in, a programming language or computer program. The terms “programming language” and “computer program,” as used herein, each include any language used to specify instructions to a computer, and include (but is not limited to) the following languages and their derivatives: Assembler, Basic, Batch files, BCPL, C, C+, C++, Delphi, Fortran, Java, JavaScript, machine code, operating system command languages, Pascal, Perl, PL1, Python, scripting languages, Visual Basic, metalanguages which themselves specify programs, and all first, second, third, fourth, fifth, or further generation computer languages. Also included are database and other data schemas, and any other meta-languages. No distinction is made between languages which are interpreted, compiled, or use both compiled and interpreted approaches. No distinction is made between compiled and source versions of aprogram. Thus, reference to a program, where the programming language could exist in more than one state (such as source, compiled, object, or linked) is a reference to any and all such states. Reference to a program may encompass the actual instructions and / or the intent of those instructions.

[0094] It should be understood that the foregoing description is only illustrative of the present disclosure. Various alternatives and modifications can be devised by those skilled in the art without departing from the disclosure. Accordingly, the present disclosure is intended to embrace all such alternatives, modifications, and variances. The embodiments described with reference to the attached drawing figures are presented only to demonstrate certain examples of the disclosure. Other elements, steps, methods, and techniques that are insubstantially different from those described above and / or in the appended claims are also intended to be within the scope of the disclosure.

Claims

What is Claimed:

1. A system for presenting prescreened in-vivo studies, the system comprising: at least one processor; and at least one memory storing instructions which, when executed by the at least one processor, cause the system at least to: access a plurality of prescreened in-vivo studies of at least a portion of gastrointestinal tracts (GIT) of patients, each of the plurality of prescreened in-vivo studies comprising respective in-vivo images; access, for each of the plurality of prescreened in-vivo studies, respective prescreening information generated prior to the respective in-vivo images being read by a healthcare professional, the respective prescreening information comprising information on whether one or more GIT conditions were indicated by image prescreening processing of the respective in-vivo images; and provide a graphical user interface configured to present a listing for each of the plurality of prescreened in-vivo studies, the graphical user interface configured to present, in each listing, one or more graphical indications corresponding to whether the one or more GIT conditions were indicated by the image prescreening processing.

2. The system of claim 1, wherein, for each of the plurality of prescreened in-vivo studies, the image prescreening processing processes the respective in-vivo images prior to any healthcare professional reading the respective in-vivo images.

3. The system of claim 1 or claim 2, wherein the image prescreening processing is performed by at least one machine learning model.

4. The system of any one of claims 1-3, wherein the one or more GIT conditions comprise bleeding, vascular disease, and inflammatory disease, and wherein the one or more graphical indications comprise a graphical indication that bleeding was indicated, a graphical indication that vascular disease was indicated, and a graphical indication that inflammatory disease was indicated.

5. The system of any one of claims 1-4, wherein the image prescreening processing is performed by at least a convolutional neural network configured to detect the one or more GIT conditions in in-vivo images.

6. The system of any one of claims 1-5, wherein the instructions, when executed by the at least one processor, further cause the system at least to: prioritize display of listings which have one or more graphical indications indicating that one or more GIT conditions were indicated; and display the listings that are prioritized for display before displaying listings that are not prioritized for display.

7. The system of any one of claims 1-6, wherein the graphical user interface further comprises control elements configured to sort the listings according to one of the one or more graphical indications selected by a user of the graphical user interface.

8. A method for presenting in-vivo studies, the method comprising: accessing a plurality of prescreened in-vivo studies of at least a portion of gastrointestinal tracts (GIT) of patients, each of the plurality of prescreened in-vivo studies comprising respective in-vivo images; accessing, for each of the plurality of prescreened in-vivo studies, respective prescreening information generated prior to the respective in-vivo images being read by a healthcare professional, the respective prescreening information comprising information on whether one or more GIT conditions were indicated by image prescreening processing of the respective in-vivo images; and providing a graphical user interface configured to present a listing for each of the plurality of prescreened in-vivo studies, the graphical user interface configured to present, in each listing, one or more graphical indications corresponding to whether the one or more GIT conditions were indicated by the image prescreening processing.

9. The method of claim 8, wherein, for each of the plurality of prescreened in-vivo studies, the image prescreening processing processes the respective in-vivo images prior to any healthcare professional reading the respective in-vivo images.

10. The method of claim 8 or claim 9, wherein the image prescreening processing is performed by at least one machine learning model.

11. The method of any one of claims 8-10, wherein the one or more GIT conditions comprise bleeding, vascular disease, and inflammatory disease, and wherein the one or more graphical indications comprise a graphical indication that bleeding was indicated, a graphical indication that vascular disease was indicated, and a graphical indication that inflammatory disease was indicated.

12. The method of any one of claims 8-11, wherein the image prescreening processing is performed by at least a convolutional neural network configured to detect the one or more GIT conditions in in-vivo images.

13. The method of any one of claims 8-12, further comprising: prioritizing display of listings which have one or more graphical indications indicating that one or more GIT conditions were indicated; and displaying the listings that are prioritized for display before displaying listings that are not prioritized for display.

14. The method of any one of claims 8-13, wherein the graphical user interface further comprises control elements configured to sort the listings according to one of the one or more graphical indications selected by a user of the graphical user interface.

15. A processor-readable medium storing instructions which, when executed by at least one processor of a system, cause the system at least to perform the method of any one of claims 8-14.

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