RSS-Feed abonnieren
DOI: 10.1055/a-1907-6569
Artificial intelligence in gastroenterology: Where are we heading?
Abstract
Background and study aims Artificial intelligence (AI) is set to impact several fields within gastroenterology. In gastrointestinal endoscopy, AI-based tools have translated into clinical practice faster than expected. We aimed to evaluate the status of research for AI in gastroenterology while predicting its future applications.
Methods All studies registered on Clinicaltrials.gov up to November 2021 were analyzed. The studies included used AI in gastrointestinal endoscopy, inflammatory bowel disease (IBD), hepatology, and pancreatobiliary diseases. Data regarding the study field, methodology, endpoints, and publication status were retrieved, pooled, and analyzed to observe underlying temporal and geographical trends.
Results Of the 103 study entries retrieved according to our inclusion/exclusion criteria, 76 (74 %) were based on AI application to gastrointestinal endoscopy, mainly for detection and characterization of colorectal neoplasia (52/103, 50 %). Image analysis was also more frequently reported than data analysis for pancreaticobiliary (six of 10 [60 %]), liver diseases (eight of nine [89 %]), and IBD (six of eight [75 %]). Overall, 48 of 103 study entries (47 %) were interventional and 55 (53 %) observational. In 2018, one of eight studies (12.5 %) were interventional, while in 2021, 21 of 34 (61.8 %) were interventional, with an inverse ratio between observational and interventional studies during the study period. The majority of the studies were planned as single-center (74 of 103 [72 %]) and more were in Asia (45 of 103 [44 %]) and Europe (44 of 103 [43 %]).
Conclusions AI implementation in gastroenterology is dominated by computer-aided detection and characterization of colorectal neoplasia. The timeframe for translational research is characterized by a swift conversion of observational into interventional studies.
** These authors share senior authorship.
Publikationsverlauf
Eingereicht: 02. Dezember 2021
Angenommen nach Revision: 20. Juli 2022
Accepted Manuscript online:
22. Juli 2022
Artikel online veröffentlicht:
15. November 2022
© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
-
References
- 1 Hann A, Troya J, Fitting D. Current status and limitations of artificial intelligence in colonoscopy. United Eur Gastroenterol J 2021; 9: 527-533
- 2 Zippel C, Bohnet-Joschko S. Rise of clinical studies in the field of machine learning: a review of data registered in ClinicalTrials.gov. Int J Environ Res Public Health 2021; 18: 5072
- 3 Chen H, Sung JJY. Potentials of AI in medical image analysis in Gastroenterology and Hepatology. J Gastroenterol Hepatol 2021; 36: 31-38
- 4 Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol 2019; 25: 1666-1683
- 5 Bisschops R, East JE, Hassan C. et al. Advanced imaging for detection and differentiation of colorectal neoplasia: European Society of Gastrointestinal Endoscopy (ESGE) Guideline – Update 2019. Endoscopy 2019; 51: 1155-1179
- 6 van der Sommen F, Zinger S, Curvers WL. et al. Computer-aided detection of early neoplastic lesions in Barrett’s esophagus. Endoscopy 2016; 48: 617-624
- 7 Swager A-F, van der Sommen F, Klomp SR. et al. Computer-aided detection of early Barrett’s neoplasia using volumetric laser endomicroscopy. Gastrointest Endosc 2017; 86: 839-846
- 8 Hirasawa T, Aoyama K, Tanimoto T. et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric cancer Off J Int Gastric Cancer Assoc Japanese Gastric Cancer Assoc 2018; 21: 653-660
- 9 Caballol B, Gudiño V, Panes J. et al. Ulcerative colitis: shedding light on emerging agents and strategies in preclinical and early clinical development. Expert Opin Investig Drugs 2021; 30: 931-946
- 10 Bonovas S, Fiorino G, Allocca M. et al. Biologic therapies and risk of infection and malignancy in patients with inflammatory bowel disease: a systematic review and network meta-analysis. Clin Gastroenterol Hepatol Off Clin Pract J Am Gastroenterol Assoc 2016; 14: 1385-1397.e10
- 11 Hafner S, Zolk K, Radaelli F. et al. Water infusion versus air insufflation for colonoscopy. Cochrane Database Syst Rev 2015; 5: CD009863
- 12 Wei Y, Shen G, Yang Y. et al. Inspection and polypectomy during both insertion and withdrawal or only during withdrawal of colonoscopy? A protocol for systematic review and meta analysis. Medicine (Baltimore) 2020; 99: e20775
- 13 Lim S, Hammond S, Park J. et al. Training interventions to improve adenoma detection rates during colonoscopy: a systematic review and meta-analysis. Surg Endosc 2020; 34: 3870-3882
- 14 Chen B-B. Artificial intelligence in pancreatic disease. World J Gastroenterol 2020; 1: 19-30