Deploying artificial intelligence to find the needle in the haystack: deep learning for video capsule endoscopy

Abstract

Wireless video capsule endoscopy (CE) is a noninvasive endoscopic technique developed in the mid 1990s that has been used to visualize a wide spectrum of pathologic conditions of the small intestine, from inflammatory enteropathies such as inflammatory bowel disease and celiac disease to suspected small-bowel bleeding, to polyposis syndromes. Recent clinical practice guidelines put forth by the American Gastroenterological Association have recommended CE in a growing number of clinical scenarios, ranging from GI bleeding to polyposis syndromes. 1 Reviewing CE results in clinical practice, however, can be challenging and labor-intensive. A typical video capsule records 2 images per second and 50,000 to 60,000 still images in a single video recording. Finding a subtle abnormality among this number of images can feel akin to looking for a needle in the proverbial haystack, with average physicians' reading times ranging from 45 to 120 minutes. 2 Several strategies have been used that aim to reduce physicians' reading times and increase accuracy. These strategies have included using an endoscopy nurse as a preliminary reader 3 and using software designed to highlight images of potential interest. 2 Artificial intelligence (AI), specifically deep learning, may offer a promising approach to streamlining the CE reading process. Over the past decade, machine learning and deep learning (a subset of machine learning) have been applied broadly to GI endoscopy, in applications ranging from colon polyp detection to dysplasia detection in Barrett's esophagus. 4 In the past year, several important studies have examined the role of deep learning in the automatic detection of erosions, ulcerations, and angioectasias in CE. 5-7 In this issue of Gastrointestinal Endoscopy, Saito et al 8 examine the role of a deep learning algorithm in the detection of protruding lesions during wireless CE. The authors developed a deep learning algorithm based on a single-shot multibox detector deep convolutional neural network to specifically detect protruding lesions in the small bowel during CE. The algorithm was developed using 30,584 images of protruding lesions from 292 patients from 3 hospitals in Japan: Sendai Kousei Hospital,