Abstract
Complete endoscopic evaluation of the small bowel is challenging due to its length and anatomy. Although several advances have been made to achieve diagnostic and therapeutic goals, including double-balloon enteroscopy, single-balloon enteroscopy, and spiral enteroscopy, video capsule endoscopy (VCE) remains the least invasive tool for complete visualization of the small bowel and is the preferred method for initial diagnostic evaluation. At present, interpretation of VCE data requires manual annotation of landmarks and abnormalities in recorded videos, which can be time consuming. Computer-assisted diagnostic systems using artificial intelligence may help to optimize VCE reading efficiency by reducing the need for manual annotation. Here we present a large VCE data set compiled from studies performed at two United States hospitals in Providence, Rhode Island, including 424 VCE studies and 5,247,588 total labeled images. In conjunction with existing published data sets, these files may aid in the development of algorithms to further improve VCE.
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