Can artificial intelligence replace endoscopists when assessing mucosal healing in ulcerative colitis? A systematic review and diagnostic test accuracy meta-analysis

Alessandro Rimondi; Klaus Gottlieb; Edward J Despott; Marietta Iacucci; Alberto Murino; Gian Eugenio Tontini

doi:10.1016/j.dld.2023.11.005

Can artificial intelligence replace endoscopists when assessing mucosal healing in ulcerative colitis? A systematic review and diagnostic test accuracy meta-analysis

Dig Liver Dis. 2023 Dec 5:S1590-8658(23)01023-X. doi: 10.1016/j.dld.2023.11.005. Online ahead of print.

Authors

Alessandro Rimondi¹, Klaus Gottlieb², Edward J Despott³, Marietta Iacucci⁴, Alberto Murino⁵, Gian Eugenio Tontini⁶

Affiliations

¹ Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom. Electronic address: alessandro.rimondi5@gmail.com.
² Eli Lilly and Company, Indianapolis, IN, United States.
³ Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom.
⁴ APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland.
⁵ Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom; Department of Gastroenterology, Cleveland Clinic London, London, United Kingdom.
⁶ Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Gastroenterology and Endoscopy unit, Milan, Italy.

PMID: 38057218
DOI: 10.1016/j.dld.2023.11.005

Abstract

Backgrounds and aims: Mucosal healing (MH) in inflammatory bowel diseases (IBD) is an important landmark for clinical decision making. Artificial intelligence systems (AI) that automatically deliver the grade of endoscopic inflammation may solve moderate interobserver agreement and the need of central reading in clinical trials.

Methods: We performed a systematic review of EMBASE and MEDLINE databases up to 01/12/2022 following PRISMA and the Joanna Briggs Institute methodologies to answer the following question: "Can AI replace endoscopists when assessing MH in IBD?". The research was restricted to ulcerative colitis (UC), and a diagnostic odds ratio (DOR) meta-analysis was performed. Risk of bias was evaluated with QUADAS-2 tool.

Results: A total of 21 / 739 records were selected for full text evaluation, and 12 were included in the meta-analysis. Deep learning algorithms based on convolutional neural networks architecture achieved a satisfactory performance in evaluating MH on UC, with sensitivity, specificity, DOR and SROC of respectively 0.91(CI95 %:0.86-0.95);0.89(CI95 %:0.84-0.93);92.42(CI95 %:54.22-157.53) and 0.957 when evaluating fixed images (n = 8) and 0.86(CI95 %:0.75-0.93);0.91(CI95 %:0.87-0.94);70.86(CI95 %:24.63-203.86) and 0.941 when evaluating videos (n = 6). Moderate-high levels of heterogeneity were noted, limiting the quality of the evidence.

Conclusions: AI systems showed high potential in detecting MH in UC with optimal diagnostic performance, although moderate-high heterogeneity of the data was noted. Standardised and shared AI training may reduce heterogeneity between systems.

Keywords: Artificial intelligence; Endoscopy; Inflammatory Bowel Disease; Ulcerative Colitis.

Publication types

Review