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CC BY 4.0 · Endosc Int Open 2024; 12(10): E1102-E1117
DOI: 10.1055/a-2403-3103
Review

Improving the endoscopic recognition of early colorectal carcinoma using artificial intelligence: current evidence and future directions

Ayla Thijssen 
1   GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands (Ringgold ID: RIN5211)
2   Department of Gastroenterology and Hepatology, Maastricht Universitair Medisch Centrum+, Maastricht, Netherlands (Ringgold ID: RIN199236)
,
Ramon-Michel Schreuder 
3   Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, Netherlands (Ringgold ID: RIN3168)
1   GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands (Ringgold ID: RIN5211)
,
Nikoo Dehghani
4   Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
,
Marieke Schor
5   University Library, Department of Education and Support, Maastricht University, Maastricht, Netherlands (Ringgold ID: RIN5211)
,
Peter H.N. de With
4   Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
,
Fons van der Sommen 
4   Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
,
Jurjen J. Boonstra
6   Department of Gastroenterology and Hepatology, Leids Universitair Medisch Centrum, Leiden, Netherlands (Ringgold ID: RIN4501)
,
Leon M.G. Moons
7   Department of Gastroenterology and Hepatology, University Medical Center Utrecht, Utrecht, Netherlands
,
Erik J. Schoon
3   Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, Netherlands (Ringgold ID: RIN3168)
1   GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands (Ringgold ID: RIN5211)
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Abstract

Background and study aims Artificial intelligence (AI) has great potential to improve endoscopic recognition of early stage colorectal carcinoma (CRC). This scoping review aimed to summarize current evidence on this topic, provide an overview of the methodologies currently used, and guide future research.

Methods A systematic search was performed following the PRISMA-Scr guideline. PubMed (including Medline), Scopus, Embase, IEEE Xplore, and ACM Digital Library were searched up to January 2024. Studies were eligible for inclusion when using AI for distinguishing CRC from colorectal polyps on endoscopic imaging, using histopathology as gold standard, reporting sensitivity, specificity, or accuracy as outcomes.

Results Of 5024 screened articles, 26 were included. Computer-aided diagnosis (CADx) system classification categories ranged from two categories, such as lesions suitable or unsuitable for endoscopic resection, to five categories, such as hyperplastic polyp, sessile serrated lesion, adenoma, cancer, and other. The number of images used in testing databases varied from 69 to 84,585. Diagnostic performances were divergent, with sensitivities varying from 55.0% to 99.2%, specificities from 67.5% to 100% and accuracies from 74.4% to 94.4%.

Conclusions This review highlights that using AI to improve endoscopic recognition of early stage CRC is an upcoming research field. We introduced a suggestions list of essential subjects to report in research regarding the development of endoscopy CADx systems, aiming to facilitate more complete reporting and better comparability between studies. There is a knowledge gap regarding real-time CADx system performance during multicenter external validation. Future research should focus on development of CADx systems that can differentiate CRC from premalignant lesions, while providing an indication of invasion depth.

Keywords

Endoscopy Lower GI Tract - Colorectal cancer - Polyps / adenomas / ... - Diagnosis and imaging (inc chromoendoscopy, NBI, iSCAN, FICE, CLE...) - Quality and logistical aspects - Image and data processing, documentatiton

Supplementary Material



Publication History

Received: 22 May 2024

Accepted after revision: 21 August 2024

Accepted Manuscript online:
26 August 2024

Article published online:
10 October 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).

Georg Thieme Verlag KG
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