Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons

Abstract

Introduction: Since the advent of artificial intelligence (AI) in clinical studies, luminal gastrointestinal endoscopy has made great progress, especially in the detection and characterization of neoplastic and preneoplastic lesions. Several studies have recently shown the potential of AI-driven endoscopy for the investigation of inflammatory bowel disease (IBD). This systematic review provides an overview of the current position and future potential of AI in IBD endoscopy. Methods: A systematic search was carried out in PubMed and Scopus up to 2 December 2020 using the following search terms: artificial intelligence, machine learning, computer-aided, inflammatory bowel disease, ulcerative colitis (UC), Crohn’s disease (CD). All studies on human digestive endoscopy were included. A qualitative analysis and a narrative description were performed for each selected record according to the Joanna Briggs Institute methodologies and the PRISMA statement. Results: Of 398 identified records, 18 were ultimately included. Two-thirds of these (12/18) were published in 2020 and most were cross-sectional studies (15/18). No relevant bias at the study level was reported, although the risk of publication bias across studies cannot be ruled out at this early stage. Eleven records dealt with UC, five with CD and two with both. Most of the AI systems involved convolutional neural network, random forest and deep neural network architecture. Most studies focused on capsule endoscopy readings in CD ( n = 5) and on the AI-assisted assessment of mucosal activity in UC ( n = 10) for automated endoscopic scoring or real-time prediction of histological disease. Discussion: AI-assisted endoscopy in IBD is a rapidly evolving research field with promising technical results and additional benefits when tested in an experimental clinical scenario. External validation studies being conducted in large and prospective cohorts in real-life clinical scenarios will help confirm the added value of AI in assessing UC mucosal activity and in CD capsule reading. Plain language summary Artificial intelligence for inflammatory bowel disease endoscopy Artificial intelligence (AI) is a promising technology in many areas of medicine. In recent years, AI-assisted endoscopy has been introduced into several research fields, including inflammatory bowel disease (IBD) endoscopy, with promising applications that have the potential to revolutionize clinical practice and gastrointestinal endoscopy. We have performed the first systematic review of AI and its application in the field of IBD and endoscopy. A formal process of paper selection and analysis resulted in the assessment of 18 records. Most of these (12/18) were published in 2020 and were cross-sectional studies (15/18). No relevant biases were reported. All studies showed positive results concerning the novel technology evaluated, so the risk of publication bias cannot be ruled out at this early stage. Eleven records dealt with UC, five with CD and two with both. Most studies focused on capsule endoscopy reading in CD patients ( n = 5) and on AI-assisted assessment of mucosal activity in UC patients ( n = 10) for automated endoscopic scoring and real-time prediction of histological disease. We found that AI-assisted endoscopy in IBD is a rapidly growing research field. All studies indicated promising technical results. When tested in an experimental clinical scenario, AI-assisted endoscopy showed it could potentially improve the management of patients with IBD. Confirmatory evidence from real-life clinical scenarios should be obtained to verify the added value of AI-assisted IBD endoscopy in assessing UC mucosal activity and in CD capsule reading.

Nomenclature and Definition of Atrophic Lesions in Small Bowel Capsule Endoscopy: A Delphi Consensus Statement of the International CApsule endoscopy REsearch (I-CARE) Group

“…This particular need is going to become even more important in the next few years, as the use of artificial intelligence (AI) software for endoscopy diagnosis is expected to markedly increase. Large CE databases with solid and shared findings between experts are keystones for effective machine learning and for building precise and trustworthy AI programs [ 24 , 25 ]. The results of our work have shown that the main features of atrophic lesions are mosaicism, scalloping, and folds reduction, and they have rapidly found consensus among readers of SBCEs, even among those who read only a few CEs of coeliac patients per year.…”

Clinical application and diagnostic accuracy of artificial intelligence in colonoscopy for inflammatory bowel disease: systematic review

“…Tontini et al recently published a systematic review on AI in all endoscopic modalities for IBD using narrative synthesis 13 . Studies using novel endoscopic techniques such as confocal laser endomicroscopy 29 , endocytoscopy 30 , monochromatic light endoscopy 31 and red density system 32 were included.…”

“…Tontini et al recently published a systematic review on AI in all endoscopic modalities for IBD using narrative synthesis [13].…”

“…Growing evidence demonstrates the potential to minimize interobserver variability in endoscopic evaluation, thus reducing variation in patient care and outcomes. A recent systematic review on this topic offers a narrative overview on the use of AI in various subtypes of gastrointestinal endoscopy, including capsule endoscopy, endomicroscopy and experimental techniques such as prototype endoscope using light emitting diode illumination [13]. A number of narrative reviews have also been published in recent years, highlighting the contemporary interest and clinical need for AI application in IBD.…”

Machine Learning Prediction Model for Inflammatory Bowel Disease Based on Laboratory Markers. Working Model in a Discovery Cohort Study

“…Considering the complexity of every aspect of IBD, from pathogenesis to therapy, artificial intelligence (AI) is a promising approach to expand the knowledge on IBD etiology, support diagnosing and stratification of IBD patients as well as implementation of precision medicine into clinical practice [ 13 ]. Further, AI-endoscopy and especially AI-driven endoscopy in IBD, although still requiring further research, holds a great promise in improving IBD assessment [ 14 , 15 ]. Moreover, it was demonstrated that machine learning methods may be applied to identify risk of therapy complications in IBD.…”