1)Leenhardt R, Vasseur P, Li C, et al. A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy. Gastrointest Endosc 89:189-194, 2019
2)Tsuboi A, Oka S, Aoyama K, et al. Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images. Dig Endosc 32:382-390, 2020
3)Aoki T, Yamada A, Kato Y, et al. Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network. J Gastroenterol Hepatol 35:1196-1200, 2020
4)Aoki T, Yamada A, Aoyama K, et al. Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc 89:357-363.e2, 2019
5)Klang E, Grinman A, Soffer S, et al. Automated detection of Crohn's disease intestinal strictures on capsule endoscopy images using deep neural networks. J Crohns Colitis 15:749-756, 2021
6)Saito H, Aoki T, Aoyama K, et al. Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc 92:144-151.e1, 2020
7)Ding Z, Shi H, Zhang H, et al. Gastroenterologist-level identification of small-bowel diseases and normal variants by capsule endoscopy using a deep-learning model. Gastroenterology 157:1044-1054.e5, 2019
8)Aoki T, Yamada A, Kato Y, et al. Automatic detection of various abnormalities in capsule endoscopy videos by a deep learning-based system:a multicenter study. Gastrointest Endosc 93:165-173.e1, 2021
9)Otani K, Nakada A, Kurose Y, et al. Automatic detection of different types of small-bowel lesions on capsule endoscopy images using a newly developed deep convolutional neural network. Endoscopy 52:786-791, 2020
10)Xie X, Xiao Y-F, Zhao X-Y, et al. Development and validation of an artificial intelligence model for small bowel capsule endoscopy video review. JAMA Netw Open 5:e2221992, 2022
11)Barash Y, Azaria L, Soffer S, et al. Ulcer severity grading in video capsule images of patients with Crohn's disease:an ordinal neural network solution. Gastrointest Endosc 93:187-192, 2021
12)Leenhardt R, Souchaud M, Houist G, et al. A neural network-based algorithm for assessing the cleanliness of small bowel during capsule endoscopy. Endoscopy 53:932-936, 2021
13)Gan T, Liu S, Yang J, et al. A pilot trial of convolution neural network for automatic retention-monitoring of capsule endoscopes in the stomach and duodenal bulb. Sci Rep 10:4103, 2020
14)Zhou T, Han G, Li BN, et al. Quantitative analysis of patients with celiac disease by video capsule endoscopy:A deep learning method. Comput Biol Med 85:1-6, 2017
15)Oh DJ, Hwang Y, Nam JH, et al. Small bowel cleanliness in capsule endoscopy:a case-control study using validated artificial intelligence algorithm. Sci Rep 12:18265, 2022
16)Aoki T, Yamada A, Aoyama K, et al. Clinical usefulness of a deep learning-based system as the first screening on small-bowel capsule endoscopy reading. Dig Endosc 32:585-591, 2020
17)Park J, Hwang Y, Nam JH, et al. Artificial intelligence that determines the clinical significance of capsule endoscopy images can increase the efficiency of reading. PLoS One 15:e0241474, 2020
18)Houdeville C, Souchaud M, Leenhardt R, et al. A multisystem-compatible deep learning-based algorithm for detection and characterization of angiectasias in small-bowel capsule endoscopy. A proof-of-concept study. Dig Liver Dis 53:1627-1631, 2021
19)Leenhardt R, Fernandez-Urien Sainz I, Rondonotti E, et al. PEACE:Perception and expectations toward artificial intelligence in capsule endoscopy. J Clin Med 10:5708, 2021