Estimating the validity of procedures

Abstract

A computer-implemented method for estimating the adequacy of a capsule endoscopy (CE) procedure includes: accessing a plurality of images of at least a portion of the gastrointestinal tract (GIT) captured by a CE imaging device during the CE procedure; accessing a plurality of characteristic measures associated with the plurality of images; determining an adequacy measure of the CE procedure based on the plurality of characteristic measures, the adequacy measure providing a measure of whether the imaging coverage provided by the plurality of images was adequate to capture an event of interest within at least a portion of the GIT, regardless of whether such event of interest is actually present within at least a portion of the GIT; and displaying an indication of the adequacy of the CE procedure based on the adequacy measure.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 075,778, filed September 8, 2020, and U.S. Provisional Patent Application No. 63 / 228,937, filed August 3, 2021, the entire contents of each of which are incorporated herein by reference.

[0002] The present disclosure relates to image analysis methods and systems, and more particularly to systems and methods for analyzing a series of images captured via a capsule endoscopy procedure to estimate the adequacy of the procedure. [Background technology]

[0003] Capsule endoscopy (CE) allows for endoscopic examination of the entire gastrointestinal tract (GIT). Capsule endoscopy systems and methods exist that are aimed at examining specific parts of the GIT, such as the small bowel (SB) or colon. CE is a non-invasive procedure that does not require the patient to be hospitalized, and the patient can continue with most of their daily activities while the capsule is in their body.

[0004] In a typical CE procedure, a patient is referred for the procedure by a physician. The patient then arrives at a medical facility (e.g., a clinic or hospital) for the procedure. A capsule, approximately the size of a multivitamin, is swallowed by the patient at the medical facility under the supervision of a medical professional (e.g., a nurse or doctor), and the patient is provided with a wearable device (e.g., a sensor belt and recorder placed in a pouch and strap placed around the patient's shoulder). The wearable device typically includes a storage device. The patient is given guidance and / or instructions and can then be released to their daily activities.

[0005] The capsule captures images as it moves naturally through the GIT. The images and additional data (e.g., metadata) are then transmitted to a recorder worn by the patient. The capsule is typically disposable and passes naturally with bowel movements. Procedural data (e.g., captured images or portions thereof and additional metadata) are stored on a storage device in the wearable device.

[0006] The wearable device is typically returned to the medical facility by the patient along with the stored procedural data. The procedural data is then typically downloaded to a computing device located at the medical facility on which the engine software is stored. The received procedural data is then processed by the engine software into a compiled study (or "study"). A study typically contains thousands (approximately 8,000-10,000) of images. The number of images processed is typically on the order of tens of thousands, with an average of approximately 100,000.

[0007] A reader (who may be a procedure supervising physician, dedicated physician, or referring physician) may access the study through a reader application. The reader then reviews the study through the reader application, evaluates the procedure, and provides their input. The time to read a study may typically take an average of 1 / 2 hour to an hour, and this reading task may be tedious, as the reader must review thousands of images. 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, interest The report may include images identified as including a condition selected by the reader, such as an evaluation or diagnosis of the patient's condition based on procedural data (i.e., research), and / or recommendations for follow-up and / or treatment provided by the reader. This report may then be forwarded to a referring physician, who may determine the required follow-up or treatment based on the report. Summary of the Invention [Means for solving the problem]

[0008] The present disclosure relates to systems and methods for analyzing a series of images of the gastrointestinal tract (GIT). More particularly, the present disclosure relates to: Interesting Events The present invention relates to systems and methods for analyzing a series of images after a capsule endoscopy (CE) procedure is completed to estimate the adequacy of the CE procedure for capturing at least one polyp (e.g., to estimate whether the imaging coverage of the series of images was adequate to visualize at least one polyp, whether or not it is actually present). In various aspects, the at least one polyp may include a significant polyp (e.g., a polyp of about 6 mm or greater in size). As described herein, the adequacy of the CE procedure may be estimated by constructing a three-dimensional view of the GIT or a portion of the GIT using images captured in vivo by a CE imaging device. decision If this is not possible, other scale and / or indicators to validate the CE procedure decision The present disclosure may be used to rule out (e.g., either by a clinician and / or automatically) a CE procedure if imaging coverage of the series of images is presumed to be inadequate for visualizing at least one polyp (whether actually present or not), thereby significantly reducing the proportion of people who are erroneously ruled out as having polyps but not having any visualized polyps by the capsule endoscopy procedure. The present disclosure may also more confidently rule out cases where no polyps are present if the CE procedure is presumed to be adequate for visualizing at least one polyp (whether actually present or not).

[0009] Although some examples are shown and described with respect to images captured within the body by a capsule endoscopy device, the disclosed techniques may be applied to images captured by other devices or mechanisms. Furthermore, so long as it is consistent, any or all of the aspects detailed herein may be used in conjunction with any or all of the other aspects detailed herein.

[0010] According to some aspects of the present disclosure, provided is a computer-implemented method for estimating the adequacy of a capsule endoscopy (CE) procedure, the method comprising: accessing a plurality of images of at least a portion of a gastrointestinal tract (GIT) captured by a CE device during a CE procedure; scale of decision The appropriateness of scale indicates a measure of the effectiveness of the CE procedure in capturing a given event in multiple images; decision and appropriateness scale on a display.

[0011] In one aspect of the present disclosure, the validity of the procedure scale is based on the given characteristics of the CE procedure decision It can be done.

[0012] In one aspect of the present disclosure, the predetermined event may include a certain type of pathology, at least one occurrence of a certain type of pathology, all occurrences of a certain type of pathology in a certain portion of the GIT, at least one occurrence of a certain type of pathology of a certain size, all occurrences of a certain type of pathology of a certain size in a certain portion of the GIT, a polyp, at least one occurrence of a polyp, all occurrences of polyps in the colon, at least one occurrence of polyps larger than a certain size in the colon, all occurrences of polyps larger than a certain size in the colon, a parasite, a disease indicator, and / or a disease appearance.

[0013] In another aspect of the present disclosure, the predetermined event may be a periodic event, a temporary event, and / or a steady event.

[0014] In another aspect of the present disclosure, the method may further include providing an indication of whether a study based on the CE procedure should be excluded from being generated, decision teeth decision Validated scale Based on.

[0015] In yet another aspect of the present disclosure, the method further comprises: decision Validated scaleThis may include excluding generating research based on.

[0016] In yet another aspect of the present disclosure, the method may further include, if a CE procedure is excluded: receiving a probability score indicating whether a predetermined event is included in the accessed image; and generating a study of the previously excluded CE procedure based on the probability score of the event exceeding a predetermined threshold.

[0017] In one aspect of the present disclosure, the validity scale is based on classical machine learning techniques, deep learning techniques, and / or heuristic methods. decision It can be done.

[0018] In one aspect of the present disclosure, the characteristic scale may include segment properties or per-procedure global properties.

[0019] In another aspect of the present disclosure, each image of the plurality of images of the GIT may be associated with one segment of a plurality of consecutive segments of the GIT, and the method further comprises: determining a segment validity of each segment of the plurality of consecutive segments of the GIT based on one or more segment characteristics. scale of decision This may include:

[0020] In another aspect of the present disclosure, the segment characteristic may be selected from the group consisting of: a motility score or a score indicative of the average level of cleaning per segment.

[0021] In yet another aspect of the present disclosure, the validity scale further based on a segment adequacy probability based on a multiplication of at least two of the motor score, the cleaning level per segment, and / or the elapsed time. decision It can be done.

[0022] In one aspect of the present disclosure, per-procedure global validity scalemay be based on the average cleansing score across all of the segments, patient demographics, the last segment of the GIT reached by the CE device, and / or the absolute time spent by the CE device in the portion of the GIT.

[0023] In another aspect of the present disclosure, the property scale may include the anatomical colon segment associated with the image, the capsule endoscopy device transition pattern, the CE device communication errors, the anatomical landmarks in the multiple images, and / or the coverage of the GIT tissue in the multiple images.

[0024] According to an aspect of the present disclosure, there is provided a system for estimating the adequacy of a capsule endoscopy (CE) procedure. The system includes a display, at least one processor, and at least one memory. The memory includes instructions stored thereon that, when executed by the at least one processor, cause the system to access a plurality of images of at least a portion of the gastrointestinal tract (GIT) captured by a CE device during the CE procedure; and to estimate the adequacy of the CE procedure. scale (Indicates a measure of the effectiveness of the CE procedure in capturing a given event in multiple images) decision and validity scale is displayed on the display.

[0025] In another aspect of the present disclosure, the validity of the procedure scale is based on the given characteristics of the CE procedure decision It can be done.

[0026] In one aspect of the present disclosure, the instructions, when executed by the at least one processor, further cause the system to provide an indication of whether a study based on the CE procedure should be excluded from being generated, decision teeth decision Validated scaleAccording to an aspect of the present disclosure, there is provided a computer-implemented method for estimating the adequacy of a capsule endoscopy (CE) procedure, the method comprising: accessing a plurality of images of at least a portion of the gastrointestinal tract (GIT) captured by a CE device during the CE procedure; scale of decision The appropriateness of scale indicates a measure of the effectiveness of the CE procedure in capturing a given event in multiple images; decision and excluding from generating studies based on CE procedures, where the validity is below a predetermined threshold. scale This includes exclusions based on:

[0027] In another aspect of the present disclosure, the method may further include indicating to the user that the CE procedure has been omitted.

[0028] In another aspect of the disclosure, the method may further include receiving an event score and including a previously excluded CE procedure within the study based on the received event score exceeding a predetermined threshold.

[0029] According to some aspects of the present disclosure, a computer-implemented method for estimating the adequacy of a capsule endoscopy (CE) procedure includes: accessing a plurality of images of at least a portion of a gastrointestinal tract (GIT) captured by a CE imaging device during the CE procedure; and determining a plurality of characteristics associated with the plurality of images. scale access to multiple properties scale Based on the validity of the CE procedure scale of decision The appropriateness of scale The imaging coverage provided by the multiple images is within at least a portion of the GIT. Interesting Events whether it was appropriate to capture scale , like this Interesting Events is actually present in at least some part of GIT, decision and appropriateness scale This includes displaying the validity indication of the CE procedure based on the

[0030] In various embodiments of the computer-implemented method, the method includes processing a plurality of images to identify a plurality of image groups, where, in each image group of the plurality of image groups, each image in the respective image group captures the same tissue region.

[0031] In various embodiments of the computer-implemented method, a plurality of characteristics scale One of the characteristics scale contains the number of images in each image group for each of the multiple image groups, and the validity of the CE procedure scale is based on the number of images in each image group of multiple image groups. decision will be done.

[0032] In various embodiments of the computer-implemented method, a plurality of characteristics scale One of the characteristics scale contains the average cleaning ratio for each image group for multiple image groups, and is used to evaluate the adequacy of the CE procedure. scale is based on the average cleaning ratio for each image group of multiple image groups. decision will be done.

[0033] In various embodiments of the computer-implemented method, the method calculates an average cleaning ratio for each group of images by accessing a mapping of cleaning scores to cleaning ratios. decision and for each image group of the plurality of image groups: accessing a cleaning score for each image within each image group; determining a cleaning ratio for each image within each image group based on the mapping of the cleaning scores to the cleaning ratio. decision and the average cleaning ratio for each image group is the average of the cleaning ratios for the images within each image group. decision This includes:

[0034] In various embodiments of the computer-implemented method, at least a portion of the GIT comprises multiple segments. scale of decision The purpose is to check the validity of each segment of multiple segments. scale of decision and the appropriateness of each segment of multiple segments scale Based on the validity of the CE procedure scale of decision This includes:

[0035] In various embodiments of the computer-implemented method, the method further comprises: scale However, the imaging coverage provided by the multiple images is within at least a portion of the GIT. Interesting Events This was not appropriate for capturing Interesting Events indicates whether or not it actually exists in at least some part of the GIT. decision The CE procedure adequacy instruction may include a statement of why the CE procedure is not appropriate. decision The statement shall include at least one reason why the change was made.

[0036] In various embodiments of the computer-implemented method, the validity of each segment of the plurality of segments is determined. scale Based on the validity of the CE procedure scale of decision To do this: Interesting Events access a priori probability of occurrence of a disease, which is empirically determined based on a patient population; decision and based on a priori probability, and the validity of each segment of the plurality of segments. scale Based on the validity of the CE procedure scale of decision This includes:

[0037] In various embodiments of the computer-implemented method, the method comprises determining at least one quality associated with the plurality of images. scale access to at least one quality scale If satisfied, a validity indication is given based on the first set of validity rules. decision and at least one quality scale If either of the above conditions is not met, the validity rule of the second set is not met. scale of decision This includes:

[0038] According to some aspects of the present disclosure, a system for estimating the adequacy of a capsule endoscopy (CE) procedure includes a display device, at least one processor, and at least one memory having instructions stored thereon, the instructions, when executed by the at least one processor, causing the system to: access a plurality of images of at least a portion of the gastrointestinal tract (GIT) captured by a CE imaging device during the CE procedure; scale access to multiple properties scale Based on the validity of the CE procedure scale of decision The appropriateness of scale The imaging coverage provided by the multiple images is at least part of the GIT. Interesting Events whether it was appropriate to capture scale like this Interesting Events whether or not it actually exists in at least some part of the GIT, decision and appropriateness scale The validity indication of the CE procedure is displayed on the display device based on the result.

[0039] In various embodiments of the system, the instructions, when executed by the at least one processor, further cause the system to process the plurality of images to identify a plurality of image groups, wherein each image group of the plurality of image groups captures the same tissue region.

[0040] In various embodiments of the system, a plurality of characteristics scale One of the characteristics scale contains the number of images in each image group for each of the multiple image groups, and is used to evaluate the validity of the CE procedure. scale is based on the number of images in each image group of multiple image groups. decision will be done.

[0041] In various embodiments of the system, a plurality of characteristics scale One of the characteristics scale contains the average cleaning ratio for each image group for multiple image groups, and is used to evaluate the adequacy of the CE procedure. scale is based on the average cleaning ratio for each image group of multiple image groups. decision will be done.

[0042] In various embodiments of the system, the instructions, when executed by the at least one processor, further cause the system to calculate an average cleaning ratio for each image group by: decision and for each image group of the plurality of image groups: accessing a cleaning score for each image within each image group; determining a cleaning ratio for each image within each image group based on the mapping of cleaning scores to cleaning ratios. decision and the average cleaning ratio for each image group is the average of the cleaning ratios for the images within the image group. decision To do.

[0043] In various embodiments of the system, at least a portion of the GIT comprises multiple segments, and the validity of the CE procedure is scale of decision To do: Validity of each segment of multiple segments scale of decision and the appropriateness of each segment of multiple segments scale Based on the validity of the CE procedure scale of decision This includes:

[0044] In various embodiments of the system, the validity of each of the plurality of segments is scale Based on the validity of the CE procedure scale of decision When the instructions are executed by the at least one processor, the instructions cause the system to: Interesting Events access a priori probability of occurrence of a disease, which is empirically determined based on a patient population; decision and based on a priori probability, and the validity of each segment of the plurality of segments. scale Based on the validity of the CE procedure scale of decision Make them do something.

[0045] In various embodiments of the system, the instructions, when executed by the at least one processor, further cause the system to: scale access to at least one quality scale If satisfied, a validity indication is given based on the first set of validity rules. decision and at least one quality scale If either of the above conditions is not met, the validity rule of the second set is not met. scale of decision Make them do something.

[0046] In various embodiments of the system, the instructions, when executed by at least one processor, cause the system to scale However, the imaging coverage provided by the multiple images is within at least a portion of the GIT. Interesting Events This was not appropriate for capturing Interesting Events indicates whether or not it actually exists in at least some part of the GIT. decision and the adequacy indication of the CE procedure will explain why the CE procedure is not appropriate. decision The statement shall include at least one reason why the change was made.

[0047] In various embodiments of the system, Interesting Events is a significant polyp, and the instructions, when executed by at least one processor, cause the system to scale However, the imaging coverage provided by the multiple images is within at least a portion of the GIT. Interesting Events This was not appropriate for capturing Interesting Events indicates whether or not it actually exists in at least some part of the GIT. decision The polyp detector processed multiple images and detected significant polyps in multiple images. decision The CE procedure adequacy instruction may indicate that the CE procedure is not appropriate. decision However, this decision may include an indication that the polyp detector has overruled the polyp.

[0048] According to some aspects of the present disclosure, a non-transitory computer-readable medium stores instructions that, when executed by a processor, cause execution of a method, the method comprising: accessing a plurality of images of at least a portion of a gastrointestinal tract (GIT) captured by a CE imaging device during a CE procedure; and acquiring a plurality of characteristics associated with the plurality of images. scale access to multiple properties scale Based on the validity of the CE procedure scale of decision The appropriateness of scale The imaging coverage provided by the plurality of images is within at least a portion of the GIT. Interesting Events whether it was appropriate to capture scale like this Interesting Events whether or not it actually exists in at least some part of the GIT, decision and appropriateness scale This includes displaying the validity indication of the CE procedure based on the

[0049] In various embodiments of the non-transitory computer readable medium, the instructions, when executed by the processor, cause further performance of a method including: determining at least one quality associated with the plurality of images; scale access to at least one quality scale If satisfied, a validity indication is given based on the first set of validity rules. decision and at least one quality scale If either of the above conditions is not met, the validity rule of the second set is not met. scale of decision To do.

[0050] Further details and aspects of exemplary embodiments of the present invention are described in more detail below with reference to the accompanying drawings.

[0051] These and other aspects and features of the present disclosure will become more apparent in view of the following detailed description taken in conjunction with the accompanying drawings, in which like reference numerals identify similar or identical elements. [Brief explanation of the drawings]

[0052] [Figure 1] 1 is a diagram showing the gastrointestinal tract (GIT). [Figure 2] FIG. 1 is a block diagram of an exemplary system for analyzing medical images captured in vivo via a capsule endoscopy (CE) procedure according to aspects of the present disclosure. [Figure 3] FIG. 1 is a block diagram of an exemplary computing device that may be used with the system of the present disclosure. [Figure 4] FIG. 1 is a diagram showing the large intestine. [Figure 5] 1 is a block diagram of an exemplary deep learning neural network, as well as inputs and outputs of the deep learning neural network, according to aspects of the present disclosure. [Figure 6] FIG. 6 is a diagram of layers of the deep learning neural network of FIG. 5 according to an embodiment of the present disclosure. [Figure 7] FIG. 1 is a block diagram of an exemplary classical machine learning classifier according to aspects of the present disclosure. [Figure 8] 3 is an exemplary image captured by a CE device according to FIG. 2 having a poor cleaning score according to an embodiment of the present disclosure. [Figure 9A] 10 is an exemplary graph of the output of a motion detector for an image of the cecum according to aspects of the present disclosure. [Figure 9B] 10 is an exemplary graph of the output of a motion detector for an image of the ascending colon according to aspects of the present disclosure. [Figure 9C] 10 is an exemplary graph of the output of a motion detector for an image of the transverse colon according to aspects of the present disclosure. [Figure 9D] 10 is an exemplary graph of the output of a motion detector for an image of the descending colon according to aspects of the present disclosure. [Figure 9E] 10 is an exemplary graph of the output of a motion detector for an image of a rectum according to aspects of the present disclosure. [Figure 10] 1 is a flowchart of an exemplary method for estimating the adequacy of a capsule endoscopy procedure according to aspects of the present disclosure. [Figure 11] 1 is a flow diagram of another exemplary method for estimating the adequacy of a capsule endoscopy procedure according to aspects of the present disclosure. [Figure 12] 1 is a flowchart of an exemplary method for identifying a group of images capturing the same tissue region according to aspects of the present disclosure. [Figure 13] 1 is a diagrammatic view of an exemplary image group according to aspects of the present disclosure. [Figure 14] 1 is a flowchart of an exemplary method for estimating an average cleaning ratio according to an aspect of the present disclosure. [Figure 15A] 10 is an exemplary histogram of the number of images of polyps with various cleaning scores according to aspects of the present disclosure. [Figure 15B] 10 is an exemplary histogram of the number of colon images with various cleansing scores according to aspects of the present disclosure. [Figure 16] 15C is a plot of exemplary cleaning ratios based on the histograms of FIGS. 15A and 15B according to embodiments of the present disclosure. [Figure 17] 1 is a flowchart of an exemplary method for estimating an adequacy measure for a capsule endoscopy procedure based on multiple segment scores according to aspects of the present disclosure. [Figure 18] 1 is a graph of an example mapping according to an aspect of the present disclosure. [Figure 19] 1 is a graph depicting example validity rules for categorizing procedures according to aspects of the present disclosure. [Figure 20] 10 is a graph depicting another set of example rules for categorizing procedures according to aspects of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0053] The present disclosure relates to systems and methods for analyzing medical images, and more particularly, to estimating the adequacy of a capsule endoscopy (CE) procedure after the CE procedure is completed (e.g., determining whether the imaging coverage provided by a series of images captured in vivo via a capsule endoscopy (CE) procedure reveals at least one polyp or other lesion). Interesting Events The present invention relates to a system and method for estimating whether a procedure was adequate to capture a CE procedure (whether or not it actually exists). Interesting Events The estimated appropriateness of a CE procedure can also be used to automatically exclude a procedure from a study if it is estimated to be inappropriate. Although some examples are shown and described with respect to images captured within the body by a CE device, the disclosed techniques may be applied to images captured by other devices or mechanisms.

[0054] The term "validity" and its derivatives as referred to herein with respect to a procedure means that the imaging coverage provided by the series of images acquired by the procedure is Interesting Events whether it was appropriate to capture the scale Refers to...

[0055] The term "excluded" and its derivatives as referred to herein with respect to procedures means that CE procedures Interesting Events The excluded procedures may include providing an indication that the CE procedure results were not adequate to capture the desired results and / or that the CE procedure results were of below a threshold level of quality. For example, the images may be very unclear and / or the results may lack many images due to connectivity issues between the CE device and the system. According to some embodiments, a CE study is not generated for the excluded procedures.

[0056] The terms "predetermined event," " Interesting Events" and their derivatives may be or include, among other things, a periodic event (such as a contraction), a temporary event (such as fresh bleeding), or a steady event (such as a polyp from the time it first appears). Interesting Events " may also include, but is not limited to, for example: a certain type of pathology, at least one occurrence of a certain type of pathology, all occurrences of a certain type of pathology in a portion of the GIT, at least one occurrence of a certain type of pathology of a certain size, all occurrences of a certain type of pathology of a certain size in a portion of the GIT, polyps, at least one occurrence of polyps, all occurrences of polyps in the colon, at least one occurrence of polyps greater than a certain size in the colon, all occurrences of polyps greater than a certain size in the colon, parasites, disease indicators or appearances, contractions, fresh bleeding, strictures, and / or disease, among others.

[0057] The term "characteristic" as used herein in relation to a procedure scale " and its derivatives refer to the presence or absence of a property. scale Or it may be or include the degree to which such a characteristic may or may not exist. scale The value of is obtained by processing a series of images captured by the CE procedure. decision Some characteristics scale can be binary (e.g., holds: exists or not), and has some properties scale It is contemplated that β may be a score (e.g., a gastrointestinal cleansing score).

[0058] In the following detailed description, specific details are set forth to provide a thorough understanding of the present disclosure. However, it will be understood by those skilled in the art that the present 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 aspects of 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 clarity, discussion of the same or similar features or elements may not be repeated.

[0059] While the disclosure is not limited in this respect, certain terms, such as "processing," "operating," "calculating," " decision Discussions utilizing terms such as "to," "determine," "analyze," or "examine" may refer to the operation and / or processing of a computer, computing platform, computing system, or other electronic computing device that manipulates and / or transforms data represented as physical (e.g., electronic) quantities in the computer's registers and / or memory into other data similarly represented as physical quantities in the computer's registers and / or memory or other information non-transitory storage medium that may store instructions for manipulating and / or processing. Although the disclosure is not limited in this respect, the terms "plurality" or "multiple" as used herein may include, for example, "multiple" or "two or more." The terms "plurality" or "multiple" may be used throughout this specification to describe two or more components, devices, elements, units, parameters, etc. The term "set," when used herein, may include one or more items. Unless expressly stated otherwise, methods described herein are not constrained to a particular order or sequence. Additionally, the described methods or some of their elements may occur or be performed simultaneously, contemporaneously, or simultaneously. The term "classification" may be used throughout this specification to refer to the decision to assign one category of a set of categories to an image / frame. The term "classification score" may be used throughout this specification to describe a vector of values ​​generated by a machine learning system / model of a set of categories that is applicable to an image / frame. The term "classification probability" may be used throughout this specification to describe converting a classification score to a value that reflects the probability that each category of the set of categories applies to the image / frame. The conversion may involve the use of other factors, values, or functions and may use one or more algorithms, including a machine learning system / model.

[0060] The term "location" and its derivatives as referred to herein with respect to an image may refer to the estimated location of the capsule along the GIT during image capture or the estimated location of a portion of the GIT shown in the image along the GIT.

[0061] The type of CE procedure may be based, inter alia, on the portion of the GIT that is of interest and is being imaged (e.g., colon or small bowel (“SB”)), or on the specific application (e.g., to monitor GI disease conditions such as Crohn's disease or for colon cancer screening). decision It can be done.

[0062] The terms "surrounding" or "adjacent" as referred to herein with respect to images (e.g., images surrounding or adjacent to another image) may relate to spatial and / or otherwise temporal characteristics unless otherwise specified. For example, an image surrounding or adjacent to another image may be an image estimated to be located near the other image along the GIT and / or an image captured near the capture time of the other image (within some threshold, e.g., within 1 or 2 centimeters, or within 1, 5, or 10 seconds).

[0063] The terms "GIT" and "portion of GIT" may each refer to or include the other depending on the context. Thus, the term "portion of GIT" may also refer to the entire GIT, and the term "GIT" may also refer to only a portion of the GIT. As used herein, the term "segmenting" may refer to identifying one or more transition points in a sequence of images.

[0064] As used herein, the terms "segmentation" or "division" may refer to the identification of one or more transition points between segments or portions of the gastrointestinal tract (GIT) within a series of images.

[0065] As used herein, the term "distal" refers to the portion of the GIT that is further from a person's mouth, while the term "proximal" refers to the portion of the GIT that is closer to a person's mouth.

[0066] The terms "image" and "frame" may refer to or include other "images" and "frames," respectively, and may be used interchangeably in this disclosure to refer to single "images" and "frames" captured by an imaging device. For convenience, the term "image" may be used more frequently in this disclosure, but it is understood that references to images should also apply to frames.

[0067] The term "classification score" or "score" may be used throughout this specification to refer to a value or vector of values ​​of a category or set of categories that are applicable to an image / frame. In various implementations, the value or vector of values ​​of a classification score or set of classification scores may be or reflect a probability. In various embodiments, the model may output a classification score that may be a probability. In various embodiments, the model may output a classification score that may not be a probability.

[0068] The term "classification probability" may be used to describe a classification score that is a probability, or to describe the conversion of a classification score that is not a probability into a value that reflects the probability that each category in a set of categories applies to an image / frame. It will be understood from the context that various references to "probability" refer to, and are shorthand for, classification probability.

[0069] As used herein, "machine learning system" refers to and includes any computing system that implements any type of machine learning. As used herein, "deep learning neural network" refers to and includes a neural network with several hidden layers that does not require feature selection or feature engineering. In contrast, a "classical" machine learning system is a machine learning system that requires feature selection or feature engineering.

[0070] Referring to FIG. 1 , an illustration of the GIT 100 is shown. The GIT 100 is an organ system within humans and other animals. The GIT 100 typically includes a mouth 102 for ingesting nutrients, salivary glands 104 for producing saliva, an esophagus 106 through which food passes with the aid of contractions, a stomach 108 for secreting enzymes and gastric acid to aid in digesting food, a liver 110, a gallbladder 112, a pancreas 114, a small intestine 116 (e.g., the small intestine) for absorbing nutrients, and a colon 400 (e.g., the large intestine) for storing water and waste products as feces prior to defecation. The colon 400 typically includes an appendix 402, a rectum 428, and an anus 430. Food ingested through the mouth is digested by the GIT to absorb nutrients, and remaining waste products are excreted through the anus 430 as feces.

[0071] Studies of various portions of the GIT 100 (e.g., SB), colon 400, esophagus 106, and / or stomach 108 may be presented via a preferred user interface. As used herein, the term "study" refers to and includes at least one set of images selected from images captured by a CE imaging device (e.g., 212, FIG. 2) during a single CE procedure performed on a particular patient and at a particular time, and may optionally include non-image information as well. The type of procedure being performed may affect which portions of the GIT 100 are studied. interest Part decision Examples of the types of procedures that may be performed include, but are not limited to, an SB procedure, a colon procedure, an SB and colon procedure, a procedure aimed specifically to present or review the SB, a procedure aimed specifically to present or review the colon, a procedure aimed specifically to present or review the colon and SB, or a procedure aimed specifically to present or review the entire GIT (esophagus, stomach, SB, and colon).

[0072] 2 shows a block diagram of a system for analyzing medical images captured in vivo via a CE procedure. The system generally includes a capsule system 210 configured to capture images of the GIT, and a computing system 300 (e.g., a local system and / or a cloud system) configured to process the captured images.

[0073] 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 advances through the GIT. The images may be stored on the CE imaging device 212 and / or transmitted to a receiving device 214 (which typically includes an antenna). In some capsule systems 210, the receiving device 214 may be placed on a patient who has swallowed the CE imaging device 212 and may take the form of, for example, a belt worn by the patient or a patch secured to the patient.

[0074] Capsule system 210 may be communicatively coupled to computing system 300 and may transmit captured images to computing system 300. Computing system 300 may process the received images using image processing techniques, machine learning techniques, and / or signal processing techniques, among other techniques. Computing system 300 may include a local computing device local to the patient and / or a patient processing facility, a cloud computing platform provided by a cloud service, or a combination of a local computing device and a cloud computing platform.

[0075] If computing system 300 includes a cloud computing platform, images captured by capsule system 210 may be transmitted online to the cloud computing platform. In various embodiments, the images may be transmitted via a receiving device 214 worn or carried by the patient. In various embodiments, the images may be transmitted via the patient's smartphone, which may be coupled to CE imaging device 212 or receiving device 214, or any other device connected to the Internet.

[0076] FIG. 3 shows a high-level block diagram of an exemplary computing system 300 that may be used with the image analysis system of the present disclosure. The computing system 300 may include a processor or controller 305, which may be or include, for example, one or more central processing unit processors (CPUs), one or more graphics processing units (GPUs or GPGPUs), a chip, or any suitable computing or calculation device, an operating system 215, memory 320, storage 330, input devices 335, and output devices 340. A module or equipment (e.g., a receiver worn on a patient) for collecting or receiving medical images collected by the CE imaging device 212 (FIG. 2), or a module or equipment (e.g., a workstation) for displaying or selecting a display, may be, include, or be executed by the computing system 300 shown in FIG. 3. A communications component 322 of the computing system 300 may enable communication with remote or external devices, for example, via the Internet or another network, wirelessly, or via a suitable network protocol such as File Transfer Protocol (FTP).

[0077] Computing system 300 includes operating system 315. Operating system 315 may be or include any code segments configured and / or designed to perform tasks involved in coordinating, scheduling, arbitrating, supervising, controlling, or otherwise managing the operation of computing system 300 (e.g., scheduling program execution). Memory 320 may be or include, for example, random access memory (RAM), read-only memory (ROM), dynamic RAM (DRAM), synchronous DRAM (SD-RAM), double data rate (DDR) memory chips, flash memory, volatile memory, non-volatile memory, cache memory, buffers, short-term memory units, long-term memory units, or other suitable memory or storage units. Memory 320 may be or include multiple, possibly different, memory units. Memory 320 may store, for example, instructions for performing methods (e.g., executable code 325) and / or data, such as user responses, interrupts, etc.

[0078] The executable code 325 can be any executable code (e.g., an application, program, process, task, or script). The executable code 325 can be executed by the controller 305, perhaps under control of the operating system 315. For example, execution of the executable code 325 can cause the display or selection of displays of medical images as described herein. In some systems, two or more computing systems 300 or two or more components of a computing system 300 can be used for multiple functions described herein. One or more computing systems 300 or two or more components of a computing system 300 can be used for the various modules and functions described herein. Devices including similar or different components to those included in the computing system 300 can be used and connected to a network and used as a system. One or more processors 305 can be configured to perform the methods of the present disclosure, for example, by executing software or code. Storage 330 may be or include, for example, a hard disk drive, a floppy disk drive, a compact disk (CD) drive, a CD-recordable (CD-R) drive, a universal serial bus (USB) device, or other suitable removable and / or fixed storage unit. Data, such as instructions, code, medical images, image streams, etc., that can be processed by controller 305 may be stored in storage 330 and loaded from storage 330 into memory 320. In some embodiments, some of the components shown in FIG. 3 may be omitted.

[0079] Input devices 335 may include, for example, a mouse, a keyboard, a touchscreen or pad, or any suitable input device. It will be appreciated that any suitable number of input devices may be operably coupled to computing system 300. Output devices 340 may include one or more monitors, screens, displays, speakers, and / or any other suitable output devices. It will be appreciated that any suitable number of output devices (as indicated by block 340) may be operably coupled to computing system 300. Any applicable input / output (I / O) devices may be operably coupled to computing system 300, and for example, a wired or wireless network interface card (NIC), a modem, a printer or facsimile machine, a universal serial bus (USB) device, or an external hard drive may be included in input devices 335 and / or output devices 340.

[0080] Multiple computer systems 300 including some or all of the components shown in Figure 3 can be used with the described systems and methods. For example, the CE imaging device 212, the receiver, the cloud-based system, and / or a workstation or portable computing device for displaying images can include some or all of the components of the computer system of Figure 3. A cloud platform (e.g., a remote server) including components such as the computing system 300 of Figure 3 can receive procedural data such as images and metadata, process and generate studies, and also display the generated studies for physician review (e.g., on a web browser running on a workstation or portable computer). An "on-premises" option can use a medical facility's workstation or local server to store, process, and display images and / or studies.

[0081] According to some aspects of the present disclosure, a user (e.g., a clinician) may build their understanding of a case by reviewing a study that includes a display of images (e.g., captured by CE imaging device 212) that have been (e.g., automatically) selected as potentially interesting images. Referring to FIG. 4, an illustration of a colon 400 is shown. The colon 400 absorbs water and stores any remaining waste as feces before being eliminated by defecation. The colon 400 may be divided into, for example, five anatomical segments: the cecum 404, the right or ascending colon 410, the transverse colon 416, the left or descending colon 422 (e.g., the left sigmoid colon 424), and the rectum 428.

[0082] The terminal ileum 408 is the final section of the SB, leading to the cecum 404 and separated from the cecum 404 by a muscular valve called the ileocecal valve (ICV) 406. The ICV 406 also connects the terminal ileum 408 to the ascending colon 410. The cecum 404 is the first section of the colon 400. The cecum 404 contains the appendix 402. The next portion of the colon 400 is the ascending colon 410. The ascending colon 410 is connected to the small intestine by the cecum 404. The ascending colon 410 runs upward through the abdominal cavity toward the transverse colon 416.

[0083] The transverse colon 416 is the portion of the colon 400 from the hepatic flexure (the bending of the colon 400 by the liver), also known as the right colonic flexure 414, to the splenic flexure (the bending of the colon 400 by the spleen), also known as the left colonic flexure 418. The transverse colon 416 is suspended from the stomach and is attached to it by a large fold of peritoneum called the omentum. Posteriorly, the transverse colon 416 is connected to the posterior abdominal wall by a mesentery known as the transverse mesocolon.

[0084] The descending colon 422 is the portion of the colon 400 from the left flexure 418 to the beginning of the sigmoid colon 426. One function of the descending colon 422 in the digestive system is to store feces to be emptied into the rectum. The descending colon 422 is also called the distal gut because it is further along the gastrointestinal tract than the proximal gut. Intestinal flora is usually very dense in this region. The sigmoid colon 426 is the portion of the colon 400 after the descending colon 422 and before the rectum 428. The name sigmoid means "S-shaped." The wall of the sigmoid colon 426 is muscular and contracts to increase pressure inside the colon 400, moving feces into the rectum 428. The sigmoid colon 426 is supplied with blood from several (usually 2-6) branches of the sigmoid artery.

[0085] The rectum 428 is the final section of the colon 400. The rectum 428 holds formed feces, awaiting removal via bowel movements.

[0086] The CE imaging device 212 (FIG. 2) can be used to image the interior of the colon 400. Entry into the colon 400 from the SB occurs through the ICV 406. Typically, after entering the colon 400 via the ICV 406, the CE imaging device 212 enters the cecum 404. However, occasionally, the CE imaging device 212 bypasses the cecum 404 and enters directly into the ascending colon 410. The colon 400 may be wide enough to allow nearly unrestricted CE imaging device 212 movement. The CE imaging device 212 can rotate and roll. The CE imaging device 212 can remain in one location for an extended period of time, move very quickly through the colon 400, or return through an earlier segment of the colon 400.

[0087] In general, the division of the GIT into anatomical segments can be performed, for example, based on identifying the passage of the CE imaging device 212 between various anatomical segments. Such identification can be performed, for example, based on machine learning techniques. Segmentation can also be performed, for example, based on interestIt is believed that this may be due to diseased and healthy segments of the body and / or due to a specific disease state and / or due to a combination of these, for example, diseases such as Crohn's disease are characterized by a diffuse disease state that spreads over a portion of the GIT in an almost "carpet-like" manner.

[0088] Referring to FIG. 5 , a block diagram of a deep learning neural network 500 for classifying images according to some aspects of the present disclosure is shown. In some systems, the deep learning neural network 500 may include a convolutional neural network (CNN) and / or a recurrent neural network. Typically, a deep learning neural network includes multiple hidden layers. As described in more detail below, the deep learning neural network 500 may utilize one or more CNNs to classify one or more images captured by the CE imaging device 212 (see FIG. 2 ) as part of the GIT. The deep learning neural network 500 may be executed on the computer system 300 ( FIG. 3 ). Those skilled in the art will understand the deep learning neural network 500 and how to implement it.

[0089] In machine learning, CNNs are a class of artificial neural networks (ANNs) that are most commonly applied to analyzing visual images. The convolutional aspect of CNNs involves applying matrix processing operations to localized portions of an image, and the result of these operations (which may involve dozens of different parallel and serial calculations) is a collection of many features delivered to the next layer. CNNs typically include convolutional layers, activation layers, deconvolutional layers (e.g., in segmentation networks), and / or pooling (usually max-pooling) layers that reduce the number of dimensions without losing too many features. Additional information may be included within the operations that generate these features. Providing unique information that yields features that inform the neural network can ultimately be used to provide an aggregate way to distinguish between various data inputs to the neural network.

[0090] FIG. 6 shows the topology of a deep learning neural network 500, which includes at least one input layer 610, multiple hidden layers 606, and at least one output layer 620. The input layer 610, multiple hidden layers 606, and output layer 620 all include neurons 602 (e.g., nodes). The neurons 602 between the various layers are interconnected via weights 604. Each neuron 602 in the deep learning neural network 500 calculates an output value by applying a special function to the input value from the previous layer. The function applied to the input value is determined by a vector of weights 604 and a bias. Learning in a deep learning neural network proceeds by iteratively adjusting these biases and weights. The deep learning neural network 500 may output logits.

[0091] Referring again to FIG. 5 , deep learning neural network 500 can be trained based on labeling training images and / or objects within the training images. For example, the images can be portions of the GIT (e.g., the rectum or cecum). In some methods according to the present disclosure, training can include supervised learning. Training can further include enhancing the training images to include adding noise, changing color, hiding portions of the training images, scaling the training images, rotating the training images, and / or stretching the training images. Those skilled in the art will understand how to train and implement deep learning neural network 500.

[0092] In some methods according to the present disclosure, a deep learning neural network 500 may be used to classify an image 502 captured by a CE imaging device 212 (see FIG. 2). The classification of the image 502 may be used to assess the adequacy of the CE procedure. scale of decision Various properties for use when scale 506 classification scores decisionFor example, image classification may include classifying an image as an image of the cecum, ascending colon, transverse colon, descending colon, or rectum. Each of the images may include a classification score for each of the consecutive segments of the GIT. The classification score may include the output (e.g., logits) of the classical machine learning classifier 700 after applying a function such as SoftMax to make the output represent a probability. scale is whether the feature exists in multiple images. scale and / or the degree to which a property is present or absent.

[0093] 7, a classical machine learning classifier 700 is shown in accordance with some aspects of the present disclosure. As used herein, the term "classical machine learning classifier" refers to a machine learning-based classifier that requires feature selection and / or feature engineering for input to the classical machine learning classifier. In contrast, a deep learning neural network is an example of a machine learning-based classifier that does not require feature engineering or feature selection. As described in more detail below, the classical machine learning classifier 700 uses various characteristics, such as an exercise score and / or a cleaning score, to generate a classifier. scale The classical machine learning classifier 700 may be configured to provide a score of . The classical machine learning classifier 700 may include a linear logistic regression classifier, a decision tree, and / or a support vector machine (SVM). In various embodiments, the classical machine learning classifier 700 does not include a CNN or other deep learning network. Those skilled in the art will understand how to implement such a classical machine learning system.

[0094] A linear logistic regression classifier is a classical machine learning classifier. A linear logistic regression classifier estimates the parameters of a logistic model that best describes the probability that each sample belongs to each class. A linear logistic regression classifier is a supervised learning model. Logistic regression estimates the parameters of a logistic model. A support vector machine is a supervised learning model that has an associated learning algorithm that analyzes the data used for classification. In various embodiments, the output of a support vector machine can be normalized between "0" and "1".

[0095] In some aspects, SoftMax can be configured to map the unnormalized output of a network (e.g., the logits of a deep learning neural network and / or a classical machine learning classifier 700) to a probability distribution over the predicted output classes of one or more classification scores (e.g., classification scores of a deep learning neural network). SoftMax is a function that takes a vector of N real numbers as input and normalizes it into a probability distribution consisting of N probabilities proportional to the exponential of the input numbers. That is, prior to applying SoftMax, some vector components may be negative or greater than 1 and may not sum to 1. However, after applying SoftMax, each component lies in the interval (0,1) and the components sum to 1 so that they can be interpreted as probabilities.

[0096] The classical machine learning classifier 700 can be trained in a supervised manner. Images of a portion of the GIT can be labeled and used as training data. Those skilled in the art will understand how to train and implement the classical machine learning classifier 700.

[0097] In some methods according to the present disclosure, the classical machine learning classifier 700 may be used to provide a classification probability for each segment of the GIT for an image captured by the CE imaging device 212 (see FIG. 2 ). The image classification probability may include each image having a classification probability for a contiguous segment of the GIT. The GIT segments may include, but are not limited to, the SB or a portion thereof (e.g., the SB may be divided according to length), or the colon or a portion thereof; for example, the colon may be divided into segments or regions such as the cecum, ascending colon, transverse colon, descending colon, and / or rectum. For example, the image classification probability may be labeled as a portion of the colon (e.g., the cecum, ascending colon, transverse colon, descending colon, and / or rectum).

[0098] Various characteristics scale These characteristics are explained below in connection with Figures 8 and 9A-E. scale may be provided by a deep learning neural network (e.g., 500, FIG. 5), and / or by a classical machine learning system (e.g., 700, FIG. 7), or by other techniques. scale The CE procedure Interesting Events The characteristics disclosed below can be used to estimate whether the data was adequate to capture the characteristics (whether present or not). scale are exemplary and other specific features are considered within the scope of this disclosure.

[0099] According to some aspects of the present disclosure, the characteristic scaleThe image may include a cleansing score indicating the degree of cleansing shown in the image. As one skilled in the art will appreciate, "cleaning" refers to removing obstructions from the gastrointestinal tract (GIT) so that the GIT can be effectively imaged. Obstructions may include, for example, feces or air bubbles, among other things. FIG. 8 shows an example image captured by a CE device having poor cleansing. The image includes a large amount of fecal residue obstructing a clear view of the GIT. According to some aspects of the present disclosure, a deep learning neural network (e.g., 500, FIG. 5) and / or a classical machine learning system (e.g., 700, FIG. 7), or another technique, may assess the degree of cleansing for each image in a series of images captured by a CE procedure. decision Those skilled in the art will appreciate that, for example, Klein A, Gizbar M, Bourke M, Ahlenstiel G. "A Validated Computerized Cleansing Score for The cleaning score was calculated using the technique described in "Video Capsule Endoscopy." Dig. Endosc. 2015;28:564-569. decision You will recognize the different ways to do this. decision Such and other techniques for doing so are considered to be within the scope of this disclosure.

[0100] According to some aspects of the present disclosure, the characteristic scaleThe motility score may include an image motility score that estimates the degree of motility experienced by the CE imaging device (e.g., 212, FIG. 2) when the CE imaging device captured the image. FIGS. 9A-9E show exemplary motility score versus time graphs for images captured within various segments of the GIT. FIG. 9A shows a graph of motility score versus time for the cecum portion of the GIT. In this graph, the CE device typically has a low motility score. In FIG. 9B, a graph of motility score versus time for the ascending colon portion of the GIT is shown. In this graph, the CE device is within the ascending portion for approximately 2 seconds and has a relatively higher motility score, averaging above 0.5. Referring to FIG. 9C, a graph of motility score versus time for the transverse portion of the colon is shown. The CE device's motility score is higher at the beginning and end of the graph. Referring to FIG. 9D, a graph of motility score versus time for the descending portion of the colon is shown. This graph covers a range of approximately 3500 seconds. The motility score for this graph is highest on average between approximately 2500 and 3000 seconds. Referring to Figure 9E, a graph of rectal motility score versus time is shown. In this graph, the average motility score is approximately zero. Those skilled in the art will recognize techniques that can be used to process images and provide motility scores (such as those described in U.S. Patent No. 8,792,691, which is incorporated herein by reference in its entirety). decision These and other techniques for determining the motor skill are contemplated as being within the scope of this disclosure. In various embodiments, the motor score is a characteristic scale In various embodiments, the characteristic scale is calculated by counting the number of frames whose motion score exceeds a given threshold. decision In various embodiments, such characteristics scale is a segment of GIT decision For example, the characteristics scale was calculated based on 40 frames of motion in the cecum. decision Such and other embodiments are contemplated as being within the scope of the present disclosure.

[0101] 8 and 9A-E are exemplary and are intended to illustrate the validity of the CE procedure. scale of decisionOther characteristics to scale are considered within the scope of the present disclosure. For example, in various embodiments, the property scale Among other things, the anatomical colon segment associated with the image, the capsule endoscopy device transition pattern, CE device communication errors, anatomical landmarks in multiple images, GIT tissue coverage in multiple images, elapsed time, a per-image indication of whether the image contains at least one polyp, and whether the capsule endoscopy device interest The information may include one or more of a time percentage indicating the time that images were captured over the period of time that was within the GIT portion, and / or a progress percentage indicating the movement of the capsule to each image and relative to the entire GIT portion that is imaged. Such and other embodiments are considered within the scope of the present disclosure.

[0102] The flowchart in FIG. 10 illustrates a computer-implemented method 1000 for estimating the adequacy of a capsule endoscopy procedure. In various embodiments, the image may include several portions of the GIT detailed above. Those skilled in the art will understand that one or more operations of method 1000 may be performed in a different order, repeated, and / or omitted without departing from the scope of the present disclosure. In some methods according to the present disclosure, some or all of the operations in the illustrated method 1000 may be performed using a capsule endoscope (e.g., a CE imaging device 212 (see FIG. 2), a receiving device 214 (see FIG. 2), and a computing system 300 (see FIG. 2)). Other variations are considered within the scope of the present disclosure. The operations of FIG. 10 will be described with respect to a computing device (e.g., computing system 300 of system 200 for analyzing medical images captured in vivo via a CE procedure (see FIG. 2) or any other suitable computing system device or location thereof, including a remotely located computing device). It will be understood that the illustrated operations are equally applicable to other systems and components thereof.

[0103] As mentioned above, the validity of the CE procedure scaleis the imaging coverage provided by the series of images captured in the CE procedure. Interesting Events whether it was appropriate to capture the scale The benefit is a reduction in false negatives, where a patient erroneously excludes a pathology because the series of images did not visualize any events or indications related to the pathology. decision If so, computing system 300 may recommend a repeat CE procedure or may provide information with a warning that a repeat procedure is recommended.

[0104] Initially, in block 1002, the operations include accessing images (e.g., a time series of images) of at least a portion of the GIT (e.g., colon 400) captured by a CE device during a CE procedure. The images may be one of the following: all of the images captured during the CE procedure and uploaded (or received) from the CE imaging device (and / or computing system 300); interest All of the images received / uploaded from the computing system 300 of the GIT portion (e.g., esophagus, SB, colon, SB, and / or colon), interest All of the images captured and received / uploaded from the computing system 300 of a given segment of an area or portion (eg, the transverse colon if the area of ​​interest is the colon).

[0105] In block 1004, this operation is performed based on one or more of the characteristics described above. scale One or more characteristics associated with the image, such as scale In some embodiments, the characteristic scale can be selected in a clinically rational manner. A rational manner provides the advantage that the rationale for excluding some CE procedures as inappropriate can be explained to clinicians, thus providing a better level of adoption for users of the technology. In some embodiments, the characteristics scale The corresponding characteristics are based on the measured correlation between the level or presence of the characteristics and the adequacy of the procedure. decisionIt can be done.

[0106] In some embodiments, the property scale is based on the image accessed as described above. decision The characteristics based on the motility score (FIGS. 9A-E) and / or the cleaning score (FIG. 8) can be or can be based on the motility score (FIG. 9A-E) and / or the cleaning score (FIG. 8). scale may be the number of images in which the motion score indicates that the CE device is moving. scale can be the average cleaning score per segment of the GIT. In some embodiments, this operation calculates the overall cleaning score for all segments of the GIT by averaging the cleaning scores for each segment of the GIT. scale of decision possible.

[0107] In some embodiments, the property scale The characteristics may include the anatomical colon segment from which the images were captured, the transition pattern of the capsule endoscopy device, CE device communication errors, anatomical landmarks within the multiple images, and / or coverage of GIT tissue within the multiple images. scale based on this disclosure, the references incorporated by reference into this disclosure, and / or knowledge in the prior art. decision You will understand how to do this.

[0108] In some embodiments, the incomplete procedure characteristic scale may be based on an indication of no visualization of the colon in the multiple images, possible visualization of the colon in the multiple images, and / or no body exit (e.g., the CE device does not exit the patient's body). scale may have a value of 1 or 0. In some embodiments, if the CE device is retained in the GIT, the score may be zero. For example, if the CE device reaches the colon, or if the captured images may only cover a portion of the colon (e.g., due to technical issues, power consumption, etc.), an incomplete procedure feature may be generated. scale may have a value of zero. In some aspects, the incomplete procedure feature scale is achieved by machine learning systems. decision It can be done.

[0109] characteristics scale Some of them are like this scale is applicable to the features of segments / parts of GIT. scale Some of the properties can be considered to be scale is a global property in the sense that it is applicable to every part of the procedure. scale Some of the properties may relate to the incomplete procedure property, as described above, which indicates that the procedure is incomplete for some reason.

[0110] In block 1006, this action is performed to determine the validity of the procedure. scale of decision In various embodiments, the adequacy of the procedure scale The validity of various segments of GIT as explained in more detail below scale , global validity per procedure scale , and / or incomplete procedural properties scale For the moment, in various embodiments, the adequacy of the procedure may be based on scale Weighted segment validity of one or more segments of the GIT scale , weighted global validity scale , and / or weighted incomplete procedure characteristics scale By multiplying decision It is sufficient to note that it can be done.

[0111] In some aspects, each image of the plurality of images of the GIT may be associated with one segment of a plurality of contiguous segments of the GIT, such as the cecum, ascending colon, transverse colon, descending colon, and / or rectum. In some aspects, this operation includes determining the segment adequacy of each segment of the plurality of contiguous segments of the GIT. scale based on one or more of the following: decisionmay include: a motility score, a per-segment cleaning score, elapsed time, a per-image indication that the image does not contain at least one polyp, interest A time percentage indicating the time that the capsule endoscopy device captured images over the period of time that it was within the GIT segment, and / or a progress percentage indicating the movement of the capsule to each image and for the entire GIT segment that was imaged. For example, this operation may generate a score indicating the average cleansing level per segment. decision Each image of a plurality of images of the GIT may be analyzed to determine whether the image contains poor cleaning. For example, the image may contain a large amount of feces, or enough feces or dark fluid to prevent reliable examination. In some embodiments, a score is calculated for each segment of the GIT. decision and then segment validity scale is based on the score for each segment decision In some embodiments, the per-segment scores can be calculated as decision In assessing the adequacy of the cecum, different characteristics of the different segments can be utilized. For example, for the cecum, the motility score can be used to assess the adequacy of the cecum segment. scale of decision and for the ascending colon segment, the cleansing score may be used to assess ascending colon segment adequacy. scale of decision In some embodiments, the per-segment validity of a segment may be used to scale is the segmental validity of the previous segment scale In some aspects, the segment validity scale is achieved by machine learning systems. decision It can be done.

[0112] In various embodiments, segment validity scale can be a product of multiplying at least two of the following: motility score, per-segment cleaning level, and / or elapsed time. While multiplication is used as an example, any other function that combines scores is contemplated. In some embodiments, the score is calculated for each segment of the GIT. decision and segment validity scaleis based on GIT's score per segment decision In some embodiments, a region score may be used. For example, the colon may be divided into two regions (e.g., by merging the first three segments as proximal segments and the last two segments as distal segments). In some embodiments, the segment probability for each segment may be based on a non-linear function of the motility score, or per-segment cleansing level, and / or elapsed time. Then, the segment validity scale may be based on multiplying all segment probabilities. In various embodiments, this multiplication may be replaced by other functions (e.g., a weighted average function). scale is the validity of the procedure scale of decision It can be used in a variety of ways to

[0113] As mentioned above, per-procedure global properties scale can be calculated for all images and for all GIT segments imaged by the CE device. In some embodiments, the global validity scale Among other things, one or more per-procedure global properties scale In some embodiments, the global validity scale may be based on the average cleansing score across all segments of the segment, patient demographics, the last segment of the GIT reached by the CE device, and / or the absolute time spent by the CE device in the portion of the GIT. Patient demographics may include, but are not limited to, age, sex, BMI, weight, height, smoking, incidence of family members who have had colorectal cancer, and / or nutritional status. For example, this operation may stomach Some interest The validity of the sequence of events decision Lower validity in female patients than in male patients scale A threshold may be used. In some embodiments, a global validity scale is achieved by machine learning systems. decision Global validity scale is the validity of the procedure scale of decision It can be used in a variety of ways to

[0114] As mentioned above, the adequacy of the procedure scale is the imaging coverage provided by the set of images captured by the CE procedure. Interesting Events whether it was appropriate to capture (whether it exists or not) scale to provide. Interesting Events An event may include a contraction, fresh bleeding, a stricture, at least one polyp (e.g., a significant polyp), and / or a disease. For example, an event may include one polyp, all polyps, and / or polyps of a particular size (e.g., 6 mm or larger). The term "disease" and its derivatives may also include syndromes (e.g., IBS), bowel disorders, etc. A disease may be diagnosed by certain visual indicators that may appear or be found in an image. child Like interest Events and other interest events are considered to be within the scope of this disclosure.

[0115] In some embodiments, the adequacy of the procedure scale is a classical machine learning technique (using scale (e.g., classical machine learning classifier 700 using scale Based on the heuristic that uses decision For example, classical machine learning techniques may include, but are not limited to, SVM and / or decision trees. For example, deep learning techniques may include CNN. Heuristic methods may include a set of rules, such as a series of if-then statements. In some embodiments, the validity of a procedure may be evaluated. scale may further include a product of multiplying at least two of the following: segment validity scale , global validity scale , and / or incomplete procedural properties scale .

[0116] In block 1008, this action decision Validated scale According to some aspects of the present disclosure, the validity scalemay be presented as a value, a color, and / or a category. Values ​​may be, for example, 0 to 1. Colors may be, but are not limited to, red / yellow / green. Categories may include, but are not limited to, appropriate / inappropriate and / or good / bad. In some embodiments, the adequacy of a CE procedure may be expressed as an appropriateness scale exceeds a predetermined threshold. decision This operation may further include providing an indication of whether the CE procedure should be omitted, where the indication is decision Validated scale For example, this action may display instructions for the clinician to rule out the CE procedure. In other aspects, the procedure may be automatically ruled out if identified as inappropriate. Based on the rule-out instructions, the clinician may decide to repeat the CE procedure or refer the patient for a colonoscopy. For example, this action may be based on a validity of 0.25. scale (e.g., value) and an indication that the CE procedure was inappropriate based on such value (e.g., if below a predetermined threshold). This action may provide the clinician with the reason the CE procedure was excluded, such as "cecal transit time too short." Other examples may include, but are not limited to: "total cleansing level too low," "capsule not passing through ascending colon," "capsule not passing through descending colon AND there were too few motion frames in the cecum AND there were too few motion frames in the ascending colon."

[0117] In some embodiments, this action may provide an indication that the CE procedure was improper and where the capsule ended. decision All images may be excluded from the study except for short clips of multiple images that may provide clinicians with the ability to

[0118] In some embodiments, this action may exclude inappropriate CE procedures. In some embodiments, this action may override the exclusion when the CE procedure actually displays an event or portion of an event. For example, in some embodiments, this action may override the exclusion decision if there is confidence that at least one significant polyp is present.

[0119] In some aspects, this operation is decision Validated scale This operation or other method or system may automatically exclude CE procedures based on the validity of the scale If the threshold is less than a predetermined threshold, a predetermined event may be detected in the images. For example, a polyp or a polyp of a predetermined minimum size may be detected in the images provided through the procedure. An event score may then be received based on this detection. A decision to overturn the procedure may be based on the event score or on the event score and the appropriateness. scale As an example, the calculation of a probability score for the presence of at least one polyp is addressed in co-pending U.S. Patent Application Publication No. 63 / 075,795, the entire contents of which are incorporated herein by reference. Other techniques for calculating an event probability score will be understood by those skilled in the art.

[0120] In some embodiments, CE procedures of relatively low quality may be excluded from the study. For example, sometimes some procedures are deemed unreasonable. scale Although it may still be "adequate" according to the procedure, its quality may be very poor (e.g., images missing many images due to connectivity problems or significant blockages in the CE procedure). Such a procedure requires a reasonable justification to prove that the procedure was adequate. scale may be excluded even if directed by

[0121] Therefore, the above indicates that the imaging coverage provided by the series of images captured by the CE procedure is Interesting Events Whether it is appropriate to capture scale (like this Interesting Events the appropriateness of the indication (whether or not it is actually present in the patient) scale As mentioned above, if it is not possible to construct a three-dimensional view of the patient's GIT or a portion of the GIT, various characteristics may be scale (such as those described above) provide an indication of whether the CE procedures are appropriate. decision Another embodiment for doing so is described below in connection with FIGS.

[0122] FIG. 11 shows a flow chart of another embodiment of a computer-implemented method 1100 for estimating the adequacy of a capsule endoscopy procedure. Those skilled in the art will understand that one or more operations of method 1000 may be performed in a different order, repeated, and / or omitted without departing from the scope of the present disclosure. The operations of FIG. 11 may be performed by a computing device for analyzing medical images captured in vivo via a CE procedure (e.g., computing system 300 of FIG. 2 or FIG. 3 ), or any other suitable computing system device or location (including a remotely located computing device). It will be understood that the illustrated operations are equally applicable to other systems and components thereof.

[0123] Beginning in block 1110, the operations include accessing images (e.g., a time series of images) of at least a portion of the GIT (e.g., colon 400) captured by a CE device during a CE procedure. The images accessed in block 1110 may be, for example, the images accessed in block 1002 of FIG. 10 described herein above.

[0124] In block 1120, this operation involves determining one or more characteristics. scale For example, the characteristics that indicate how many images capture the same tissue region. scale (Figure 12), characteristics that indicate cleaning ratio scale (Fig. 16), and / or characteristics indicating the number of distinct views scale Various characteristics including (Figure 18) scale are described in more detail later herein. In some embodiments, the properties scale may further include demographic information of the patient undergoing the CE procedure. Demographic information may include, for example, age and / or gender.

[0125] In block 1130, this operation involves determining one or more characteristics. scale Based on the validity of the CE procedure scale of decision As mentioned above, appropriateness scale is the imaging coverage provided by the set of images captured by the CE procedure. Interesting Events whether it was appropriate to capture the scale In some embodiments, the appropriateness scale Among other things, classical machine learning techniques (i.e., scale and / or by deep learning techniques (such as deep learning classifier 500) using the characteristics as input. scale Based on the heuristic that uses decision It is possible. scale of decision An example of this is described in more detail later in this specification.

[0126] In block 1170, this operation generates a quality indicator for the CE procedure. scale Quality scale may include, for example, an average cleansing score across all segments of the GIT, patient demographics, the last segment of the GIT reached by the CE device, CE device communication errors, suspicious retention of the CE device within the GIT, or the absolute time spent by the CE device within a portion of the GIT, among other indicators of the quality of the CE procedure and / or the captured images. scale may compare the time the CE device spent in the left colon versus the time the CE device spent in the right colon to determine whether the CE procedure and / or the captured images met quality criteria. decision In order to decision Criteria and / or thresholds are quality scale Other exemplary qualities may be used in conjunction with scale may include excluding and / or warning if a predetermined number of segments or less (e.g., three segments) are reached according to the GIT segmentation algorithm 1720 (FIG. 17), excluding and / or warning if all of the particular segments of the GIT are not reached by the CE device, and / or excluding or warning if the total GIT elapsed time is less than a predetermined period (e.g., about 10 minutes).

[0127] For example, time in the right and / or left colon may affect quality scale and by using the GIT segmentation algorithm to distinguish between images captured in the right colon and images captured in the left colon. decision The timestamps associated with such images may indicate the amount of time the CE imaging device was in the right colon and / or the amount of time the CE imaging device was in the left colon (as described in connection with FIG. 17). decision In various embodiments, if the time in the left colon and / or the time in the right colon does not meet some threshold, the quality may be assessed. scale cannot be satisfied.

[0128] The average cleaning score on the GIT is scale and, for example, by accessing the cleaning score for each image and averaging the cleaning scores across all images in the manner described herein above decision In various embodiments, if the average cleaning score across all GITs does not meet some threshold, the quality is determined. scale cannot be satisfied.

[0129] Technical failure is a quality scale And whether too many images are lost decision For example, the operation may compare the percentage of missing images to a predetermined threshold. For example, the operation may calculate the percentage of missing images from the total images, and if this percentage is greater than about 25%, the quality scale Other percentages may be quality scale It can be used for

[0130] Questionable retention of CE imaging devices by GIT is of poor quality scaleThis operation may determine whether there is a suspected retention of a CE device in the GIT based on detected segment transitions, an indication of no visualization of the colon in the multiple images, possible visualization of the colon in the multiple images, and / or no body prolapse (e.g., if the CE device does not exit the patient's body). decision In some embodiments, any suspected retention of a CE device in the GIT may result in a quality scale cannot be satisfied.

[0131] The above qualities scale The thresholds and conditions are exemplary, and other qualities scale and thresholds or conditions are considered within the scope of this disclosure.

[0132] In block 1150, this operation is decision Validated scale , the quality accessed in block 1170 scale , and applying a set of validity rules that consider the output of polyp detector 1160. Polyp detector 1160 may process the images accessed in block 1110 and may operate to identify with high confidence images that contain polyps. An example of a polyp detector 1160 is described in U.S. Patent Application Publication No. 63 / 075,795, which is incorporated herein by reference in its entirety.

[0133] Continuing with reference to block 1150, in various embodiments, the validity rules determine the validity of the CE procedure based on the rules described in connection with FIGS. decision In various embodiments, quality scale If any of the validity rules are not satisfied, the validity rules may provide an indication that the procedure was improper (as described in connection with FIG. 20). scale or quality scale However, if the procedure was improper, but the polyp detector identifies the image of at least one polyp with high confidence, then the validity rule 1150 will indicate that the procedure was improperly overridden by the polyp detector. decision It is deemed inappropriate by decision Such validity rules are exemplary, and variations are contemplated within the scope of this disclosure. For example, in various embodiments, the operations of FIG. 11 may not include polyp detector 1160, and therefore may not include validity rules. scale or quality scale In various embodiments, the operations of FIG. scale Such and other variations are considered to be within the scope of the present disclosure.

[0134] In block 1140, this action is validated. decision This includes displaying the CE procedure. decision If so, this action may provide one or more reasons why the procedure was inappropriate. For example, decision Reasons for not performing the CE procedure may include, among others: the colon was not visualized, short elapsed time, poor cleansing, technical failure (such as communication gaps), the right and / or left colon was not visualized, and / or the right and / or left colon was only partially visualized. decision If so, this action may display an indication to the clinician that the CE procedure was appropriate and that the CE procedure is included within the study. In other embodiments, a procedure may be automatically excluded if identified as inappropriate. Based on the exclusion indication, the clinician may decide to repeat the CE procedure or refer the patient for a colonoscopy. This action may provide the clinician with the reason the CE procedure was excluded. For example, "cecal elapsed time was too short." Other examples may include, but are not limited to: "colon not visualized (retained)," "right colon not visualized," and "left colon not visualized AND elapsed time was short AND there was a communication error."

[0135] characteristics scale , validity scale ,quality scale Specific examples of validity rules are described below.

[0136] FIG. 12 illustrates the characteristics of the CE device by identifying groups of images that may be captured while the device was stationary or slowly moving and therefore may capture the same tissue region. scale 12 is a flowchart of a method 1200 for providing a GIT repository that efficiently manages many different "views" of GIT based on progress scores. decision where each image group corresponds to a different view of the GIT.

[0137] Initially, in block 1202, the operation designates a new image group. In block 1204, the operation accesses the next image in a series of images (e.g., a time series of images) of at least a portion of the GIT captured by the CE device during the CE procedure. In block 1206, the operation accesses a progress score for the image that indicates the movement of the CE device within the GIT as the image was captured. As noted above, those skilled in the art will understand how to create a progress score, such as the technique described in U.S. Pat. No. 8,792,691, incorporated herein by reference above. decision You will become aware of the techniques to

[0138] At block 1208, the operation determines whether the progress score of the image is greater than a predetermined threshold. decision A lower progress score may indicate less or no movement, while a higher progress score may be an indicator of greater movement. If the image's progress score is below a predetermined threshold, the image may be considered to capture the same view / tissue region of the GIT and may be included in a group, and the operation returns to block 1204, where the next image is accessed. If the image's progress score is greater than a predetermined threshold, the image may be considered to capture a different view / tissue region of the GIT, and therefore the operation may return to block 1202 and designate this image as the start of a new group / view of the GIT. The operation of FIG. 12 continues until all images in the series of images captured by the CE procedure have been processed. The operation of FIG. 12 is exemplary, and other techniques for identifying groups of images that may capture the same view are considered within the scope of this disclosure.

[0139] FIG. 13 shows an example of an image group resulting from the operations of FIG. 12. FIG. 13 shows a series of images 1300. A first group of images 1310 includes one or more images whose progress scores 1314 are all below a predetermined threshold (not shown). Thus, each group of images 1310 corresponds to little or no movement in the GIT and can be considered to provide a "view" of the same tissue region. In the example shown, the first group 1310 includes six images 1316 that are all part of a particular group number 1312 (e.g., group "1"). Each of the images 1316 in the first group 1310 has a progress score (e.g., 1314) that is below a predetermined threshold.

[0140] In the illustrated example, the seventh image 1310b has a progress score greater than the predetermined threshold and is therefore designated as part of the second group. The operations of Figure 12 continue by processing the images 1300 and grouping the images based on their progress scores. In the illustrated example, the 12 images 1300 have been grouped into seven groups. Thus, the 12 images 1300 can be considered to provide seven different views of the GIT.

[0141] In the example of Figure 13, the first group contains six images, while each of the other groups contains a single image. According to some aspects of the present disclosure, more images of the same "view" of the GIT may be added to the same group within a particular view. Interesting Events This increases the probability of identifying a particular image (e.g., a polyp). Therefore, the number of images in a group Interesting Events A characteristic that represents the probability of imaging a particular object (e.g., a polyp) scale In various embodiments, the number of images in a group may be Interesting Events can be converted into the probability of imaging the characteristic scale For example, in various embodiments, a group containing a single image may be Interesting Events While a group containing six images may have a certain probability (e.g., a 15% probability) of capturing Interesting Events12 and 13. The characteristics provided by FIGS. 12 and 13 may have a very high probability (e.g., 90% probability) of capturing an image, and similarly for different numbers of images within a group. The probability numbers are exemplary, and different probability numbers are considered within the scope of this disclosure. scale is valid scale , which is described in more detail below.

[0142] FIG. 14 shows a characteristic referred to herein as the average cleaning ratio. scale 14 is a flowchart of a method for providing a method for generating a .times. ... decision In block 1404, the operation accesses the cleaning score of each image in the group. As discussed above, those skilled in the art will recognize that the cleaning score of an image can be calculated by decision (See, among other techniques, Klein A, Gizbar M, Bourke M, Ahlenstiel G. "A Validated Computerized Cleansing Score for This will include the techniques described in "Video Capsule Endoscopy." Dig. Endosc. 2015;28:564-569.

[0143] In block 1406, the operation calculates the cleaning ratio for each image in the group. decision Cleaning ratios are described in relation to Figures 15A, 15B and 16. For example, in the first group of images 1310 of Figure 13, each of the six images will have an associated cleaning ratio. In block 1408, this operation calculates the average cleaning ratio of the images in the group. decision The average cleaning ratio for each group is scale It could be.

[0144] Figure 15A shows Interesting Events15A is a histogram of the number of images in the series of images that contain a polyp (e.g., a polyp) and are tabulated by cleaning score for each of the various cleaning scores, and FIG. 15B is a histogram of the number of images in the entire series of images that are tabulated by cleaning score for each of the various cleaning scores. According to some aspects of the present disclosure, the histograms in FIGS. 15A and 15B have been normalized to have the same Y-axis range. In various embodiments, the Y-axis range may be a probability range of [0, 1] so that FIGS. 15A and 15B may be considered probability distributions. For generality, the normalized histogram in FIG. 15A may be referred to as " Interesting Events The normalized histogram in FIG. 15A will be referred to as the "full frame histogram," and the normalized histogram in FIG. 15B will be referred to as the "full frame histogram." The Y-axis value of each portion of the normalized histogram in FIGS. 15A, 15B will be referred to as the "normalized height." As used herein, the cleaning ratio is the ratio:( Interesting Events This refers to the normalized height of the cleaning score of the histogram) / (normalized height of the cleaning score of the all-frame histogram).

[0145] 16 is a plot of cleaning ratios 1602 across cleaning scores, where cleaning ratios 1602 are indicated by open circles. In various embodiments, regression analysis can be used to fit a curve 1604 to the plotted cleaning ratios 1602 to map cleaning scores to cleaning ratios. In the example shown, the fitted curve 1604 is a third-order polynomial. However, the fitted curve can be any polynomial of any order.

[0146] According to some embodiments of the present disclosure, the term "cleaning ratio" can refer to either a plotted cleaning ratio 1602 or a fitted cleaning ratio curve 1604. Also referring to FIG. 14, a cleaning score is accessed for each image in the group, and a cleaning ratio (e.g., 1602, 1604, FIG. 16) is calculated for each image in the group based on the cleaning score. decision As mentioned above, the average cleaning ratio for the group is characteristic scale It could be.

[0147] The illustrated embodiments of Figures 15A, 15B, and 16 are exemplary, and several variations are considered within the scope of the present disclosure. For example, in various embodiments, separate histograms and cleansing ratio plots for different portions of the GIT may be generated. For example, with respect to the colon, different segments may have different cleansing behavior. Typically, most images under study are from the cecum, as the CE imaging device spends most of its time in the cecum during an average CE procedure. Separate histograms and cleansing ratio plots / fitted curves may be generated for different colon segments (such as separate histograms and cleansing ratio plots / fitted curves for the cecum, the right or ascending colon, the transverse colon, the left or descending colon, and the rectum). These and other variations are considered within the scope of the present disclosure.

[0148] Therefore, the above description of Figures 12-16 applies to each image group / viewpoint. Interesting Events Various characteristics, including the probability of imaging (e.g., polyps) and the mean cleaning ratio for each imaging group / perspective scale According to some aspects of the present disclosure, for each image group / viewpoint, further characteristics are provided. scale :( in the group Interesting Events (Probability of imaging) × (average cleaning ratio of the group) decision and such scale is sometimes referred to herein as the "group score."

[0149] In some embodiments of the present disclosure, the validity of the CE procedure scale 12. A larger sum of group scores may indicate that there are more multi-frame views of the GIT with acceptable cleaning, and a smaller sum of group scores may indicate that there are fewer multi-frame views of the GIT and / or that the cleaning was suboptimal. In various embodiments, the sum of group scores may be mapped to a probability, as shown in the example of FIG. 18, and the probability may indicate the adequacy. scaleThe mapping shown in Figure 18 is exemplary. In various embodiments, the mapping in Figure 18 may be empirically derived from training data and / or validation data. decision The mapping may be calculated, fitted to and / or extrapolated from the data, or may be arbitrary based on the desired mapping.

[0150] In some aspects, the mapping shown in FIG. 18 may be provided based on a receiver operating characteristics (ROC) curve. As one skilled in the art will recognize, an ROC curve is a graph that shows the performance of a classification model at various classification thresholds. For purposes of generating the mapping of FIG. 18, the classification model is configured to classify each sum of group scores into one of the following two categories: Interesting Events To capture this (such Interesting Events A "positive" classification was appropriate (regardless of whether or not the image coverage provided by the image was Interesting Events To capture this (such Interesting Events A "negative" classification was inappropriate (whether or not a positive result actually exists). When a particular threshold is used to make the classification, the classification model will have a particular true positive rate (TPR) and a particular false positive rate (FPR). Different thresholds will result in different TPRs and FPRs, and in various embodiments, the different thresholds may span the entire range of possible values ​​for the group score sum. As one skilled in the art will understand, an ROC curve is generated by plotting these pairs of FPRs and TPRs for various thresholds in a two-axis coordinate space (where the X-axis represents the false positive rate (FPR) and the Y-axis represents the true positive rate (TPR)), and then interpolating between the plotted coordinates or fitting a curve to the plotted coordinates. The ROC curve may be the fitted curve, or the plotted coordinates together with interpolation between the plotted coordinates, or some combination of both.

[0151] According to some aspects of the present disclosure, an ROC curve for a classification model that classifies the sum of group scores as appropriate or inappropriate can be used to generate the mapping of Figure 18. As discussed above, the ROC curve is generated from various thresholds that can span the range of possible values ​​for the sum of group scores. Thus, each threshold can be considered in some sense a proxy for a particular sum of group scores, and the true positive rate corresponding to the threshold is a function of the imaging coverage provided by the images. Interesting Events To capture this (such Interesting Events The mapping in Figure 18 can therefore be used to map the sum of group scores to the appropriateness of the scale can be used to map the σ to a probability that can be used as

[0152] The mapping of Figure 18 and the embodiment described in connection with Figure 18 are exemplary. Other embodiments are considered within the scope of this disclosure. For example, as described below in connection with Figure 17, different sums of group scores may be calculated for various segments of the GIT, and each segment of the GIT may have a corresponding mapping such as the mapping shown in Figure 18.

[0153] FIG. 17 illustrates the relevance of image groups spanning different GIT segments, such as different parts of the colon (e.g., cecum, right or ascending colon, transverse colon, left or descending colon, and rectum). scale of decision For convenience, the following paragraphs may be described with reference to the colon portion, but the following description is intended to apply to other GIT portions as well.

[0154] Figure 17 shows the validity when multiple GIT segments exist. scale 17 is a flowchart of a method for providing a method for implementing the present invention. The operations of FIG. 17 may be performed by a computing system such as computing system 300 of FIGS. 2 and 3. In block 1702, the operations are performed by the operations of FIG. decisionThe operation accesses the image groups associated with the image sequence. In block 1704, the operation associates each image group with a GIT segment based on input from a GIT segmentation algorithm 1720, which divides the image sequence to correspond to the portion of the GIT where the image was captured. Generally, the GIT segmentation algorithm 1720 may be based, for example, on the identification of various landmarks or transition indicators between various anatomical segments. Such identification may be based, for example, on machine learning techniques. One way of segmenting the image sequence to correspond to anatomical segments is described in U.S. Patent Application Publication No. 17 / 244,988, which is incorporated herein by reference in its entirety.

[0155] In block 1706, the operation calculates a segment score for each GIT segment (e.g., cecum, ascending colon, etc.). decision The segment score of each GIT segment can be, for example, the sum of group scores described herein above, where only the image groups that are part of the GIT segment are used for the sum of group scores.

[0156] In block 1708, this operation converts each segment score into a mapped probability corresponding to the sum of the group scores, as described above in connection with Figure 18. Each segment of the GIT may have a different mapping as shown in Figure 18, which may be generated by using the ROC curve for each segment. In such an embodiment, each GIT segment may have a different mapping as shown in Figure 18, which may be generated by using the ROC curve for each segment. Interesting Events Imaging coverage of the GIT segment provided by the images corresponding to the GIT segment (e.g., at least one polyp or significant polyp) in the GIT segment, regardless of whether or not a polyp is actually present in the patient. Interesting Events For example, the probabilities for the colon segments (cecum, right or ascending colon, transverse colon, left or descending colon, and rectum) may be [P1,...,P5], and such probabilities may be the result of block 1708.

[0157] In block 1710, this operation is performed to validate the CE procedure. scale as a weighted sum of the probabilities of the GIT segments decision For example, if the probabilities for colon segments (cecum, right or ascending colon, transverse colon, left or descending colon, and rectum) are [P1,...,P5], then the weighted sum is

number

[0158] [Table 1]

[0159] Validity of CE procedures scale can be calculated as a weighted sum as follows: (0.9*0.08)+(0.8*0.22)+(0.7*0.16)+(1.0*0.38)+(0.0*0.16)=0.74. The specific values ​​in the above examples are exemplary and other values ​​are considered within the scope of this disclosure.

[0160] In various embodiments, another appropriate scale can be calculated for other parts of the GIT by using a priori probabilities. Continuing with the colon as an example, different plausibility scale may be calculated for the left side of the colon (e.g., the descending sigmoid colon and rectum) and for the right side of the colon (e.g., the cecum, ascending colon, and transverse colon). According to some aspects of the present disclosure, the a priori probabilities of the left side of the colon may be renormalized to 1, such that 0.38 for the descending sigmoid and 0.16 for the rectum become approximately 0.7 for the descending sigmoid and 0.3 for the rectum. Validity of the left side of the colon scale is calculated as (1.0 * 0.7) + (0.0 * 0.16) = 0.7. Similarly, the a priori probabilities of the right side of the colon can be renormalized to 1 so that 0.08 for the cecum, 0.22 for the ascending colon, and 0.16 for the transverse colon become approximately 0.17 for the cecum, 0.48 for the ascending colon, and 0.35 for the transverse colon. scale can be calculated as (0.9*0.17)+(0.8*0.48)+(0.7*0.35)=0.782. The colon is used merely as an example, and therefore the disclosed technique uses a priori probabilities to estimate the validity of various parts of the GIT. scale of decision It can be applied to other parts of the GIT to improve the decision If so, the validity of various parts of GIT scale can be used to explain which part of the GIT caused the CE procedure to be inappropriate.

[0161] Therefore, the above description is based on various characteristics scale Examples of such characteristics scale Validity based on scale 19 and 20 show the validity rules (1150, FIG. 11) (validity scale 1130), quality scale 19 and 20 graphically illustrate examples of the outputs of polyp detector 1160 and block 1170. For convenience, the embodiments of Figures 19 and 20 refer to plausibility as plausibility probabilities, which may be the probabilities output by block 1710 of Figure 17 or the probabilities mapped by block 1710 of Figure 18, among other possibilities. scale According to some aspects of the present disclosure, the graph in FIG. scaleis satisfied, and the graph in Figure 20 shows scale It can be applied whenever ≠ 0.01 is not satisfied.

[0162] Referring to Figure 19, the graph shows that all the quality scale 19 depicts a combination of polyp probability values ​​(e.g., 1160, FIG. 11 ) and validity probability values ​​(e.g., 1130, FIG. 11 ) used by validity rules to classify a CE procedure as appropriate, inappropriate, or inappropriate but overridden when a validity rule (e.g., 1170, FIG. 11 ) is satisfied. Each "o" 1910 is a plot of the validity probability of a CE procedure versus the polyp probability that at least one polyp was visualized by the CE procedure. Each "x" 1912 is a plot of the validity probability of a CE procedure versus the polyp probability that the CE procedure did not visualize at least one polyp. In the illustrated example, if the validity probability is 0.2 or less (region 1904), or if the validity probability is 0.4 or less and the polyp probability is 0.01 or less (region 1908), this action indicates that the CE procedure was inappropriate. If the validity probability is within the range 0.4 to 1.0 (region 1902), this action indicates that the CE procedure was appropriate. scale If is in the range of 0.2 to 0.4 and the polyp probability is greater than 0.01 (region 1906), this action indicates that the CE procedure was inappropriate but was overturned. This indicates that the CE procedure was inappropriate based on the plausibility probability, but the inappropriateness decision is overturned based on the polyp probability. Therefore, this action is used to exclude the results of the CE procedure when there is confidence. decision may be overturned based on a polyp probability that at least one significant polyp is present. As noted above, this operation may also display to the clinician the rationale for why the inappropriate result was overturned. As indicated by the "O" and "X" markers in the graph, some of the decisions categorizing the CE procedure (such as some markers in regions 1906 and 1902) may not match what actually occurred in the CE procedure, but most categorizations are correct. Interesting EventsSince it seems impractical to manually scrutinize all images of a CE procedure to identify decision may improve physician confidence in the results of CE procedures.

[0163] The regions 1902-1908 and values ​​shown in Figure 19 are exemplary, and several variations are considered within the scope of the present disclosure. For example, each region may be defined by lower and upper thresholds for plausibility probability and / or lower and upper thresholds for polyp probability. Such lower and upper thresholds may have different values ​​than those shown in Figure 19. Such and other variations are considered within the scope of the present disclosure.

[0164] Figure 20 shows that no quality scale 11 is a graph illustrating the combination of polyp probability values ​​(e.g., 1160, FIG. 11) and validity probability values ​​(e.g., 1130, FIG. 11) used by validity rules to classify a CE procedure as appropriate, inappropriate, or inappropriate but overridden if neither of the validity rules (e.g., 1170, FIG. 11) is satisfied. In the illustrated example, if the validity probability is less than or equal to 0.2 (region 2004) or if the polyp probability is less than or equal to 0.01 (region 2008), this action indicates that the CE procedure was inappropriate. scale If is in the range of 0.2 to 1 and the polyp probability is greater than 0.01 (region 2006), this action indicates that the CE procedure was inappropriate but was overturned. This means that the CE procedure was inappropriate based on the plausibility probability but was overturned. decision indicates that the polyp probability is overturned.

[0165] The regions 2004-2008 and values ​​shown in Figure 20 are exemplary, and several variations are considered within the scope of the present disclosure. For example, each region may be defined by lower and upper thresholds for plausibility probability and / or lower and upper thresholds for polyp probability. Such lower and upper thresholds may have different values ​​than those shown in Figure 20. Such and other variations are considered within the scope of the present disclosure.

[0166] In various embodiments, all qualities scale is satisfied (e.g., Figure 19) and any quality scale Rather than having one set of validity rules where all validity rules are not satisfied (e.g., FIG. 20), three or more sets of validity rules may be used. For example, different validity rules may be used to determine whether a particular quality scale is not satisfied. These and other variations are considered to be within the scope of this disclosure.

[0167] Although some examples are shown and described with respect to images captured within the body by a CE device, the disclosed techniques may be applied to images captured by other devices or mechanisms.

[0168] The embodiments disclosed herein are examples of the present disclosure and may therefore be embodied in various forms. For example, although some embodiments herein are described as separate embodiments, each of the embodiments herein may be combined with one or more of the other embodiments herein. The specific structural and functional details disclosed herein should not be construed as limitations, but as a basis for the claims and as a representative basis for teaching those skilled in the art to employ the present disclosure in various ways in almost any appropriate detailed structure. Similar reference numerals may refer to similar or identical elements throughout the accompanying drawing descriptions.

[0169] The phrases "in one embodiment," "in an embodiment," "various embodiments," "in some embodiments," or "in other embodiments" may each refer to one or more of the same or different embodiments according to the present disclosure. A phrase of the form "A or B" means "(A), (B), or (A and B)." A phrase of 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)."

[0170] Any of the operations, methods, programs, algorithms, or code described herein may be converted into or expressed in a programming language or computer program embodied on a computer or machine-readable medium. As used herein, the terms "programming language" and "computer program" each include any language used to specify instructions to a computer, and include (but are 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 instruction languages, Pascal, Perl, PL1, Python, scripting languages, Visual Basic, metalanguages ​​that themselves specify programs, and all first-, second-, third-, fourth-, fifth-, or sixth-, or later-generation computer languages. Also included are databases, other data schemas, and any other metalanguages. No distinction is made between interpreted languages, compiled languages, or languages ​​that use both compiled and interpreted techniques. No distinction is made between compiled and source versions of a program. Thus, a reference to a program in a programming language that may exist in more than one state (such as a source version, a compiled version, an object version, or a linked version) is a reference to each and every such state. A reference to a program may include the actual instructions and / or the intent of those instructions.

[0171] It should be understood that the foregoing description is merely illustrative of the present disclosure. Where consistent, any or all aspects of an aspect detailed herein may be used in combination with any or all aspects of the other aspects detailed herein. Various alternatives and modifications may be devised by those skilled in the art without departing from the present disclosure. Accordingly, the present disclosure is intended to embrace all such alternatives, modifications, and variations. The embodiments described with reference to the accompanying drawings are presented to demonstrate only some examples of the present disclosure. Other elements, steps, methods, and techniques that are substantially different from those described above and / or that are within the scope of the appended claims are also intended to be within the scope of the present disclosure.

[0172] Although several embodiments of the present disclosure are illustrated in the accompanying drawings, it is not intended that the disclosure be limited to certain embodiments, as it is intended that the disclosure be as broad as the prior art will permit and that the specification be read in a similar manner. Accordingly, the above description should be construed as merely illustrative of particular embodiments, and not limiting. Those skilled in the art will envision other modifications within the scope and spirit of the claims appended hereto. (Item 1) 1. A computer-implemented method for estimating the adequacy of a capsule endoscopy (CE) procedure, comprising: accessing a plurality of images of at least a portion of the gastrointestinal tract (GIT) captured by a CE imaging device during a CE procedure; A plurality of characteristics associated with the plurality of images scale access to; the plurality of characteristics scale Based on the validity of the CE procedure scale of decision and scale the imaging coverage provided by the plurality of images is within the at least a portion of the GIT. Interesting Events whether it was appropriate to capture scale like this Interesting Events is actually present in said at least a portion of said GIT, decision and The validity of the above scale and displaying an indication of validity of the CE procedure based on the result. (Item 2) 2. The computer-implemented method of claim 1, further comprising processing the plurality of images to identify a plurality of image groups, wherein each image group of the plurality of image groups captures the same tissue region. (Item 3) the plurality of characteristics scale One of the characteristics scale includes, for each image group of the plurality of image groups, the number of images in the respective image group, and the validity of the CE procedure scale is based on the number of images in each of the plurality of image groups. decision Item 3. The computer-implemented method of item 2, (Item 4) the plurality of characteristics scale One of the characteristics scale includes, for each image group of the plurality of image groups, a mean cleaning ratio for the respective image group; and scale is based on the average cleaning ratio for each image group of the plurality of image groups. decision Item 3. The computer-implemented method of item 2, (Item 5) Accessing the mapping of cleaning scores to cleaning ratios; and For each image group of said plurality of image groups: accessing a cleaning score for each image in said respective group of images; determining a cleaning ratio for each image in each image group based on the mapping of cleaning scores to cleaning ratios; decision To do so, and the average cleaning ratio for each image group as the average of the cleaning ratios for the images within the respective image group; decision To do The average cleaning ratio for each image group is calculated by decisionItem 5. The computer-implemented method of item 4, further comprising: (Item 6) The validity of the above scale wherein the imaging coverage provided by the plurality of images is within the at least a portion of the GIT. Interesting Events This was not appropriate for capturing Interesting Events is actually present in said at least a portion of said GIT, decision Item 1. The computer-implemented method of item 1, further comprising: The indication of the adequacy of the CE procedure shall include a statement of why the CE procedure is not appropriate. decision and at least one reason why the method was performed. (Item 7) the at least a portion of the GIT includes a plurality of segments; The validity of the CE procedure scale of decision To do is The validity of each of said plurality of segments scale of decision To do so, and The validity of each segment of the plurality of segments. scale Based on the validity of the CE procedure scale of decision Item 1. The computer-implemented method of item 1, comprising: (Item 8) The validity of each segment of the plurality of segments. scale The validity of the CE procedure based on scale of decision To do is Within each of the plurality of segments Interesting Events accessing a priori probabilities of occurrence of decision to be accessed; and based on the a priori probability, and the plausibility of each segment of the plurality of segments. scale Based on the validity of the CE procedure scale of decision8. The computer-implemented method of claim 7, comprising: (Item 9) At least one quality associated with the plurality of images. scale access to; said at least one quality scale is satisfied, the validity indication is updated based on a first set of validity rules. decision and said at least one quality scale If any of the above conditions is not satisfied, the validity rule is determined to be invalid based on the second set of validity rules. scale of decision Item 1. The computer-implemented method of item 1, further comprising: (Item 10) 1. A system for estimating the adequacy of a capsule endoscopy (CE) procedure, the system comprising: Display devices; at least one processor; and at least one memory containing instructions stored thereon; The instructions, when executed by the at least one processor, cause the system to: accessing a plurality of images of at least a portion of the gastrointestinal tract (GIT) captured by a CE imaging device during a CE procedure; A plurality of characteristics associated with the plurality of images scale access to; the plurality of characteristics scale Based on the validity of the CE procedure scale of decision and scale the imaging coverage provided by the plurality of images is within the at least a portion of the GIT. Interesting Events whether it was appropriate to capture scale like this Interesting Events is actually present in said at least a portion of said GIT, decision and The validity of the above scale and displaying on the display device an indication of the validity of the CE procedure based on the result of the test. (Item 11) Item 11. The system of item 10, wherein the instructions, when executed by the at least one processor, further cause the system to process the plurality of images to identify a plurality of image groups, wherein each image group of the plurality of image groups captures the same tissue region. (Item 12) the plurality of characteristics scale One of the characteristics scale includes, for each image group of the plurality of image groups, the number of images in the respective image group, and the validity of the CE procedure scale is based on the number of images in each of the plurality of image groups. decision Item 12. The system according to item 11, (Item 13) the plurality of characteristics scale One of the characteristics scale includes, for each image group of the plurality of image groups, a mean cleaning ratio for the respective image group; and scale is based on the average cleaning ratio for each image group of the plurality of image groups. decision Item 12. The system according to item 11, (Item 14) The instructions, when executed by the at least one processor, further provide the system with: Accessing the mapping of cleaning scores to cleaning ratios; and For each image group of said plurality of image groups: accessing a cleaning score for each image in said respective group of images; determining a cleaning ratio for each image in each image group based on the mapping of cleaning scores to cleaning ratios; decision To do so, and the average cleaning ratio for each image group as the average of the cleaning ratios for the images within the respective image group; decision To do, The average cleaning ratio for each image group is calculated by decision Item 14. The system according to Item 13, (Item 15) the at least a portion of the GIT includes a plurality of segments; The validity of the CE procedure scale of decision To do is The validity of each of said plurality of segments scale of decision To do so, and The validity of each segment of the plurality of segments. scale Based on the validity of the CE procedure scale of decision Item 11. The system of item 10, comprising: (Item 16) The validity of each segment of the plurality of segments. scale The validity of the CE procedure based on scale of decision When doing so, The instructions, when executed by the at least one processor, cause the system to: Within each of the plurality of segments Interesting Events accessing a priori probabilities of occurrence of decision to be accessed; and based on the a priori probability, and the plausibility of each segment of the plurality of segments. scale The validity of the CE procedure based on scale of decision Item 16. The system according to item 15, (Item 17) The instructions, when executed by the at least one processor, further provide the system with: At least one quality associated with the plurality of images. scale access to; said at least one quality scale is satisfied, the validity indication is updated based on a first set of validity rules. decision and said at least one quality scale If any of the above conditions is not satisfied, the validity rule is determined to be invalid based on the second set of validity rules. scale of decision Item 11. The system according to item 10, (Item 18) The instructions, when executed by the at least one processor, further provide the system with: "The validity of the above scale wherein the imaging coverage provided by the plurality of images is within the at least a portion of the GIT. Interesting Events This was not appropriate for capturing Interesting Events "Whether or not a file actually exists within at least said portion of the GIT" decision Let, The adequacy indication of the CE procedure may include a reason why the CE procedure is not appropriate. decision Item 11. The system of item 10, including at least one reason why the change was made. (Item 19) The aforementioned Interesting Events is a significant polyp, The instructions, when executed by the at least one processor, further provide the system with: The validity of the above scale wherein the imaging coverage provided by the plurality of images is within the at least a portion of the GIT. Interesting Events This was not appropriate for capturing Interesting Events is actually present in at least said portion of said GIT, decision and a polyp detector that processes the plurality of images and detects that a significant polyp is detected in the plurality of images; decision Let, The indication of adequacy of the CE procedure indicates that the CE procedure is not appropriate. decision However, decision Item 11. The system of item 10, including an indication that the polyp detector has overridden the (Item 20) 1. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause performance of a method, the method comprising: accessing a plurality of images of at least a portion of the gastrointestinal tract (GIT) captured by a CE imaging device during a CE procedure; A plurality of characteristics associated with the plurality of images scale access to; the plurality of characteristics scale Based on the validity of the CE procedure scale of decision and scale the imaging coverage provided by the plurality of images is within the at least a portion of the GIT. Interesting Events whether it was appropriate to capture scale like this Interesting Events is actually present in said at least a portion of said GIT, decision and The validity of the above scale and displaying an indication of validity of the CE procedure based on the result of the test. (Item 21) The instructions, when executed by the processor, At least one quality associated with the plurality of images. scale access to; said at least one quality scale is satisfied, the validity indication is updated based on a first set of validity rules. decision and said at least one quality scale If any of the above conditions is not satisfied, the validity rule is determined to be invalid based on the second set of validity rules. scale of decision To do, 21. The non-transitory computer-readable medium of item 20, causing further execution of the method, including:

Claims

1. 1. A computer-implemented method for estimating the adequacy of a capsule endoscopy (CE) procedure, the computer-implemented method comprising: accessing a plurality of images of at least a portion of the gastrointestinal tract (GIT) captured by a CE imaging device during a CE procedure; accessing a plurality of characteristic measures associated with the plurality of images; determining a validity measure of the CE procedure based on the plurality of characteristic measures, the validity measure indicating whether imaging coverage provided by the plurality of images was adequate to capture an event of interest within the at least a portion of the GIT, regardless of whether such an event of interest actually exists within the at least a portion of the GIT; displaying an indication of the adequacy of the CE procedure based on the adequacy measure; Including, determining the validity measure of the CE procedure is based on at least one of classical machine learning techniques, deep learning techniques, or heuristic methods; The computer-implemented method comprises: determining that the appropriateness measure indicates that the imaging coverage provided by the plurality of images was not adequate to capture an event of interest within the at least a portion of the GIT, regardless of whether such event of interest actually exists within the at least a portion of the GIT. wherein the indication of the appropriateness of the CE procedure includes at least one reason why the CE procedure was determined to be inappropriate.

2. 2. The computer-implemented method of claim 1, further comprising processing the plurality of images to identify a plurality of image groups, wherein, in each image group of the plurality of image groups, each image in the respective image group captures the same tissue region.

3. 3. The computer-implemented method of claim 2, wherein one of the plurality of characteristic measures includes, for each image group of the plurality of image groups, a number of images in the respective image group, and the appropriateness measure of the CE procedure is determined based on the number of images in each image group of the plurality of image groups.

4. 3. The computer-implemented method of claim 2, wherein one characteristic measure among the plurality of characteristic measures includes, for each image group of the plurality of image groups, an average cleaning ratio for the respective image group, and the measure of adequacy of the CE procedure is determined based on the average cleaning ratio for each image group of the plurality of image groups.

5. Accessing a mapping of cleaning scores to cleaning ratios; For each of the plurality of image groups, accessing a cleaning score for each image in said respective group of images; determining a cleaning ratio for each image in the respective group of images based on the mapping of cleaning scores to cleaning ratios; determining the average cleaning ratio for each image group as an average of the cleaning ratios for the images within the respective image group; determining the average cleaning ratio for each group of images by performing The computer-implemented method of claim 4 further comprising:

6. the at least a portion of the GIT comprises a plurality of segments; Determining the validity measure of the CE procedure comprises: determining a validity measure for each segment of the plurality of segments; determining a validity measure for the CE procedure based on the validity measure for each segment of the plurality of segments; The computer-implemented method of claim 1 , comprising:

7. determining the validity measure of the CE procedure based on the validity measure of each segment of the plurality of segments, accessing an a priori probability of occurrence of the event of interest within each segment of the plurality of segments, the a priori probability being empirically determined based on a patient population; determining a plausibility measure for the CE procedure based on the a priori probability and based on the plausibility measure for each segment of the plurality of segments; The computer-implemented method of claim 6, comprising:

8. accessing at least one quality measure associated with the plurality of images; determining the indication of validity based on a first set of validity rules when the at least one quality metric is satisfied; determining the validity measure based on a second set of validity rules if any of the at least one quality measure is not satisfied; and The computer-implemented method of claim 1 further comprising:

9. 1. A system for estimating the adequacy of a capsule endoscopy (CE) procedure, the system comprising: A display device; at least one processor; at least one memory containing instructions stored thereon; Including, The instructions, when executed by the at least one processor, cause the system to: accessing a plurality of images of at least a portion of the gastrointestinal tract (GIT) captured by a CE imaging device during a CE procedure; accessing a plurality of characteristic measures associated with the plurality of images; determining a validity measure of the CE procedure based on the plurality of characteristic measures, the validity measure indicating whether imaging coverage provided by the plurality of images was adequate to capture an event of interest within the at least a portion of the GIT, regardless of whether such an event of interest actually exists within the at least a portion of the GIT; displaying on the display device an indication of the adequacy of the CE procedure based on the adequacy measure; Let them do this, determining the validity measure of the CE procedure is based on at least one of classical machine learning techniques, deep learning techniques, or heuristic methods; The instructions, when executed by the at least one processor, cause the system to: determining that the appropriateness measure indicates that the imaging coverage provided by the plurality of images was not adequate to capture an event of interest within the at least a portion of the GIT, regardless of whether such event of interest actually exists within the at least a portion of the GIT. wherein the indication of the appropriateness of the CE procedure includes at least one reason why the CE procedure was determined to be inappropriate.

10. 10. The system of claim 9, wherein the instructions, when executed by the at least one processor, further cause the system to process the plurality of images to identify a plurality of image groups, wherein each image group of the plurality of image groups captures the same tissue region.

11. 11. The system of claim 10, wherein one characteristic measure among the plurality of characteristic measures includes, for each image group of the plurality of image groups, a number of images in the respective image group, and the appropriateness measure of the CE procedure is determined based on the number of images in each image group of the plurality of image groups.

12. 11. The system of claim 10, wherein one characteristic measure among the plurality of characteristic measures includes, for each image group of the plurality of image groups, a mean cleaning ratio for the respective image group, and wherein the measure of adequacy of the CE procedure is determined based on the mean cleaning ratio for each image group of the plurality of image groups.

13. The instructions, when executed by the at least one processor, cause the system to: Accessing a mapping of cleaning scores to cleaning ratios; For each of the plurality of image groups, accessing a cleaning score for each image in said respective group of images; determining a cleaning ratio for each image in the respective group of images based on the mapping of cleaning scores to cleaning ratios; determining the average cleaning ratio for each image group as an average of the cleaning ratios for the images within the respective image group; determining the average cleaning ratio for each group of images by performing The system of claim 12 , further comprising:

14. the at least a portion of the GIT comprises a plurality of segments; Determining the validity measure of the CE procedure comprises: determining a validity measure for each segment of the plurality of segments; determining the validity measure of the CE procedure based on the validity measure of each segment of the plurality of segments; The system of claim 9 , comprising:

15. determining the validity measure of the CE procedure based on the validity measure for each segment of the plurality of segments; The instructions, when executed by the at least one processor, cause the system to: accessing an a priori probability of occurrence of the event of interest within each segment of the plurality of segments, the a priori probability being empirically determined based on a patient population; determining the plausibility measure of the CE procedure based on the a priori probability and based on the plausibility measure for each segment of the plurality of segments; The system of claim 14 .

16. The instructions, when executed by the at least one processor, cause the system to: accessing at least one quality measure associated with the plurality of images; determining the indication of validity based on a first set of validity rules when the at least one quality metric is satisfied; determining the validity measure based on a second set of validity rules if any of the at least one quality measure is not satisfied; and The system of claim 9 , further comprising:

17. the event of interest is a significant polyp; The instructions, when executed by the at least one processor, cause the system to: determining that significant polyps are detected in the plurality of images by a polyp detector that processes the plurality of images; 10. The system of claim 9, further comprising: a step of: determining whether the CE procedure is appropriate; and wherein the indication of the appropriateness of the CE procedure includes an indication that the CE procedure was determined to be inappropriate but the determination was overturned by a polyp detector.

18. 1. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause performance of a method, the method comprising: accessing a plurality of images of at least a portion of the gastrointestinal tract (GIT) captured by a CE imaging device during a CE procedure; accessing a plurality of characteristic measures associated with the plurality of images; determining a validity measure of the CE procedure based on the plurality of characteristic measures, the validity measure indicating whether imaging coverage provided by the plurality of images was adequate to capture an event of interest within the at least a portion of the GIT, regardless of whether such an event of interest actually exists within the at least a portion of the GIT; displaying an indication of the adequacy of the CE procedure based on the adequacy measure; Including, determining the validity measure of the CE procedure is based on at least one of classical machine learning techniques, deep learning techniques, or heuristic methods; The instructions, when executed by the processor, determining that the appropriateness measure indicates that the imaging coverage provided by the plurality of images was not adequate to capture an event of interest within the at least a portion of the GIT, regardless of whether such event of interest actually exists within the at least a portion of the GIT. and wherein the indication of the appropriateness of the CE procedure includes at least one reason why the CE procedure was determined to be inappropriate.

19. The instructions, when executed by the processor, accessing at least one quality measure associated with the plurality of images; determining the indication of validity based on a first set of validity rules when the at least one quality metric is satisfied; determining the validity measure based on a second set of validity rules if any of the at least one quality measure is not satisfied; and 20. The non-transitory computer-readable medium of claim 18, causing further performance of the method including:

Images