Abstract: In accordance with aspects of the present disclosure, a system includes at least one processor and at least one memory storing instructions which, when executed by the processor(s), cause the system to access images of a portion of a gastrointestinal tract (GIT) captured by a capsule endoscopy device; for each of the images, provide, by a deep learning neural network, scores for classifying the image to each of consecutive segments of the GIT; classify each image of a subset of the images, whose scores satisfy a confidence criterion, to one of the consecutive segments of the GIT; refine the classifications of the images in the subset by processing a signal over time corresponding to the classifications of the images in the subset; and estimate, among the images in the subset, a transition (1010) between two adjacent segments of the GIT based on the refined classifications of the images in the subset.

Abstract: Systems and methods may display and/or provide analysis of a number of selected images of a patient's gastrointestinal tract collected in-vivo by a swallowable capsule. Images may be displayed for review (e.g., as a study) and/or for further analysis by a user. A subset of images representing the stream of images and automatically selected according to a first selection method may be displayed. On user input, additional images corresponding to a currently displayed image may be displayed, where the additional images are automatically selected according to a second selection method. The second selection method may be based on a relation between images of the stream of in-vivo images and the currently displayed image.

Abstract: Systems and methods may display and/or provide analysis of a number of selected images of a patients gastrointestinal tract collected in-vivo by a swallowable capsule. Images may be displayed for review (e.g., as a study) and/or for further analysis by a user. A subset of images representing the stream of images and automatically selected according to a first selection method may be displayed. On user input, additional images corresponding to a currently displayed image may be displayed, where the additional images are automatically selected according to a second selection method. The second selection method may be based on a relation between images of the stream of in-vivo images and the currently displayed image.

Abstract: A system for assessing gastrointestinal tract cleansing includes one or more processors and at least one memory storing instructions. This instructions, when executed by the one or more processors, cause the system to access a plurality of images of at least a portion of a gastrointestinal tract (GIT) captured by a capsule endoscopy device; access a cleansing score for each image of the plurality of images; for each image of the plurality of images, access advancement information indicative of physical advancement of the capsule endoscopy device relating to the respective image, determine a cleansing measure for at least the portion of the GIT based on the cleansing score and the advancement information for each image of the plurality of images, and provide a visual indication based on at least one of: the cleansing measure for at least the portion of the GIT or at least one of the cleansing scores.

Abstract: In accordance with aspects of the present disclosure, a system includes at least one processor and at least one memory storing instructions which, when executed by the processor(s), cause the system to access images of a portion of a gastrointestinal tract (GIT) captured by a capsule endoscopy device; for each of the images, provide, by a deep learning neural network, scores for classifying the image to each of consecutive segments of the GIT; classify each image of a subset of the images, whose scores satisfy a confidence criterion, to one of the consecutive segments of the GIT; refine the classifications of the images in the subset by processing a signal over time corresponding to the classifications of the images in the subset; and estimate, among the images in the subset, a transition (1010) between two adjacent segments of the GIT based on the refined classifications of the images in the subset.

Abstract: Systems and methods may display and/or provide analysis of a number of selected images of a patients gastrointestinal tract collected in-vivo by a swallowable capsule. Images may be displayed for review (e.g., as a study) and/or for further analysis by a user. A subset of images representing the stream of images and automatically selected according to a first selection method may be displayed. On user input, additional images corresponding to a currently displayed image may be displayed, where the additional images are automatically selected according to a second selection method. The second selection method may be based on a relation between images of the stream of in-vivo images and the currently displayed image.