Abstract: A system for image inpainting of in-vivo images includes at least one processor and at least one memory storing instructions. The instructions, when executed by the processor(s), cause the system to access an in-vivo image of a portion of a gastrointestinal tract where the in-vivo image includes image regions to be reconstructed, process the in-vivo image by a trained image inpainting deep learning model to provide a reconstructed in-vivo image, and provide the reconstructed in-vivo image to a device for viewing by a medical professional.

Abstract: A computer-implemented method for estimating a size of a suspected polyp in an image includes accessing an image of in at least a portion of a gastrointestinal tract (GIT) captured by a capsule endoscopy device, wherein the image includes a suspected polyp; receiving an indication to an approximated periphery of the suspected polyp in the image; determining, without human intervention, a 3D measurement of the polyp based on peripheral points in the approximated periphery indication; and estimating a size of the suspected polyp based on the determined 3D measurement.

Abstract: A method for detecting indicators of a disease characterized by a presence of villous atrophy in images of a gastrointestinal tract (GIT), includes accessing a consecutive set of images of a portion of the GIT comprising a small bowel. Each image is associated with one or more classification scores, and each classification score is indicative of the associated image including a respective indicator of a disease characterized by the presence of villous atrophy. The method further includes selecting a subset of images from the consecutive set of images based on the one or more classification scores of each image of the consecutive set of images, identifying a segment of images which includes all of the images that show a proximal portion of the small bowel, selecting a plurality of images from the identified segment of images that represent the proximal portion of the small bowel, and displaying the selected images.

Abstract: The present disclosure relates to systems and methods for determining whether two images of a gastrointestinal tract (GIT) contain the same occurrence of an event indicator or different occurrences of an event indicator. An exemplary processing system includes at least one processor and at least one memory storing instructions. When the instruction are executed by the processor(s), they cause the processing system to access a first image and a second image of a portion of a GIT, where the first image and the second image contain at least one occurrence of an event indicator, and to classify the first image and the second image by a classification system configured to provide an indication of whether the first image and second image contain a same occurrence of the event indicator or contain different occurrences of the event indicator.

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.