References
- Iddan G, Meron G, Glukhovsky A, et al. Wireless capsule endoscopy. Nature 2000;405:417.
- Yamamoto H, Sekine Y, Sato Y, et al. Total enteroscopy with a nonsurgical steerable double-balloon method. Gastrointest Endosc 2001;53:216-220.
- Pennazio M, Spada C, Eliakim R, et al. Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) Clinical Guideline. Endoscopy 2015;47:352-376.
- Mussetto A, Fuccio L, Dari S, et al. MiroCam capsule for obscure gastrointestinal bleeding: a prospective, single centre experience. Dig Liver Dis 2013;45:124-128.
- Kim ER. Roles of capsule endoscopy and device-assisted enteroscopy in the diagnosis and treatment of small-bowel tumors. Clin Endosc 2020;53:410-416.
- Beg S, Card T, Sidhu R, et al. The impact of reader fatigue on the accuracy of capsule endoscopy interpretation. Dig Liver Dis 2021;53:1028-1033.
- Piccirelli S, Milluzzo SM, Bizzotto A, et al. Small bowel capsule endoscopy and artificial intelligence: first or second reader? Best Pract Res Clin Gastroenterol 2021;52-53:101742.
- Chan HP, Samala RK, Hadjiiski LM, et al. Deep learning in medical image analysis. Adv Exp Med Biol 2020;1213:3-21.
- Dray X, Iakovidis D, Houdeville C, et al. Artificial intelligence in small bowel capsule endoscopy: current status, challenges and future promise. J Gastroenterol Hepatol 2021;36:12-19.
- LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-444.
- Soffer S, Klang E, Shimon O, et al. Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis. Gastrointest Endosc 2020;92:831-839.
- 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 2020;32:382-390.
- Leenhardt R, Vasseur P, Li C, et al. A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy. Gastrointest Endosc 2019;89:189-194.
- Mohan BP, Khan SR, Kassab LL, et al. High pooled performance of convolutional neural networks in computer-aided diagnosis of GI ulcers and/or hemorrhage on wireless capsule endoscopy images: a systematic review and meta-analysis. Gastrointest Endosc 2021;93:356-364.
- Klang E, Barash Y, Margalit RY, et al. Deep learning algorithms for automated detection of Crohn's disease ulcers by video capsule endoscopy. Gastrointest Endosc 2020;91:606-613.
- 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 2021;15:749-756.
- Vicnesh J, Wei JKE, Ciaccio EJ, et al. Automated diagnosis of celiac disease by video capsule endoscopy using DAISY Descriptors. J Med Syst 2019;43:157.
- 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 2020;92:144-151.
- Trasolini R, Byrne MF. Artificial intelligence and deep learning for small bowel capsule endoscopy. Dig Endosc 2021;33:290-297.
- 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 2019;157:1044-1054.
- 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 2020;52:786-791.
- Dimas G, Spyrou E, Iakovidis DK, et al. Intelligent visual localization of wireless capsule endoscopes enhanced by color information. Comput Biol Med 2017;89:429-440.
- Leenhardt R, Souchaud M, Houist G, et al. A neural network-based algorithm for assessing the cleanliness of small bowel during capsule endoscopy. Endoscopy 2021;53:932-936.
- Nam JH, Hwang Y, Oh DJ, et al. Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy. Sci Rep 2021;11:4417.
- 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 2020;32:585-591.
- Akerman PA, Cantero D. Severe complications of spiral enteroscopy in the first 1750 patients. Gastrointest Endosc 2009;69:PAB127.
- Neuhaus H, Beyna T, Schneider M, et al. Novel motorized spiral enteroscopy: first clinical case. VideoGIE 2016;1:32-33.
- Beyna T, Arvanitakis M, Schneider M, et al. Motorised spiral enteroscopy: first prospective clinical feasibility study. Gut 2021;70:261-267.
- Prasad M, Prasad VG, Sangameswaran A, et al. A spiraling journey into the small bowel: a case series of novel motorized power spiral enteroscopies. VideoGIE 2020;5:591-596.
- Beyna T, Arvanitakis M, Schneider M, et al. Total motorized spiral enteroscopy: first prospective clinical feasibility trial. Gastrointest Endosc 2021;93:1362-1370.
- Ramchandani M, Rughwani H, Inavolu P, et al. Diagnostic yield and therapeutic impact of novel motorized spiral enteroscopy in small-bowel disorders: a single-center, real-world experience from a tertiary care hospital (with video). Gastrointest Endosc 2021;93:616-626.
- Al-Toma A, Beaumont H, Koornstra JJ, et al. The performance and safety of motorized spiral enteroscopy, including in patients with surgically altered gastrointestinal anatomy: a multicenter prospective study. Endoscopy 2022 Feb 28 [Epub]. https://doi.org/10.1055/a-1783-4802.
- Beyna T, Moreels T, Arvanitakis M, et al. Motorized spiral enteroscopy: results of an international, multicenter, prospective observational clinical study on patients with normal and altered gastrointestinal anatomy. Endoscopy 2022 Apr 21 [Epub]. https://doi.org/10.1055/a-1831-6215.
- Beyna T, Schneider M, Hollerich J, et al. Motorized spiral enteroscopy-assisted ERCP after Roux-en-Y reconstructive surgery and bilioenteric anastomosis: first clinical case. VideoGIE 2020;5:311-313.