Capsule endoscopy (CE) has revolutionized the investigation of various small-bowel abnormalities. About 10 devices fabricated by 5 companies are commercially available. As the capsule travels through the GI tract, thousands of pictures are automatically captured and transmitted to a recorder. Although the procedure is minimally invasive, physicians must review over 10,000 images per patient, which is obviously very time consuming. Physicians also fear the risk of oversight; any abnormality may be evident in only a few frames. Computer-aided diagnosis would be extremely helpful.Despite several attempts, reliability remains unacceptable. For example, the QuickView mode, originally used to remove uninformative CE images of the PillCam system, is not often used in practice because of unacceptably high miss rates for noteworthy abnormalities. Systems based on conventional machine-learning methods (eg, support vector machines, neural networks, or binary classifiers) are not yet commercially available, probably because they remain inaccurate. The difficulties include the relatively poor image quality caused by inaccurate focusing; light limitations; low resolution; the presence of bile, debris, and bubbles; and the fact that various types of abnormalities must be detected. Recently, state-of-the-art deep learning-based methods have significantly improved recognition performance in various medical fields and are expected to resolve the abovementioned problems in CE reading. Convolutional neural networks (CNNs) lead the field.In this systematic review with a meta-analysis in Gastrointestinal Endoscopy, Mohan et al 1 report a high pooled performance of CNN-based systems in terms of computer-aided diagnosis of GI ulcers and/or hemorrhage on CE images. From the 9 studies included in the final analysis, 20, 24, 23, 9, and 9 datasets were extracted to calculate accuracy, sensitivity, specificity, the positive predictive value, and the negative predictive value, respectively. All studies were retrospective in nature. The CNN systems exhibited a pooled accuracy of 95.4% (95% confidence interval [CI], 94.3-96.3), a sensitivity of 95.5% (95% CI, 94-96.5), a specificity of 95.8% (95% CI, 94.7-96.6), a positive predic-The abstracted data pertained to ulcers, nonbleeding angioectasias, and hemorrhages identified on CE images. Although these are the most common small-bowel abnormalities, CE can also identify other serious findings, including protruding lesions such as smallbowel adenocarcinomas, lymphomas, and polyps.