Artificial Intelligence and Capsule Colonoscopy: Automatic Assessment of Intestinal Preparation Scores/degree
Year: 2024
Type: article
Abstract: Introduction Medicine is evolving toward increasingly less invasive diagnostic modalities, with Gastroenterology being a prime example of this trend. The diagnostic assessment of the colon using capsule endoscopy, through specific protocols for colon capsule endoscopy or as part of panendoscopic evaluations, has gained growing importance as a first-line diagnostic method. Proper bowel preparation is essential for conclusive examinations, given the non-invasive nature of capsule endoscopy—which does not allow for interventional measures to improve mucosal visibility. Several scales have already been developed to classify bowel preparation for colon capsule endoscopy. However, their applicability is limited by inter-observer variability. Objective The aim of this study was to develop a deep-learning algorithm for the automatic classification of colon bowel preparation. Results The algorithm achieved highly satisfactory results, with sensitivity, specificity, and diagnostic accuracy of 91%, 97%, and 95%, respectively. The area under the curve ranged from 0.92 to 0.97. Conclusion This study is pivotal for the future widespread use of capsule endoscopy in colon evaluation and for minimally invasive panendoscopic modalities. Publication History Article published online: 25 April 2025 © 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution 4.0 International License, permitting copying and reproduction so long as the original work is given appropriate credit (https://creativecommons.org/licenses/by/4.0/) Thieme Revinter Publicações Ltda. Rua do Matoso 170, Rio de Janeiro, RJ, CEP 20270-135, Brazil
Source: Journal of Coloproctology
Authors: Joana Mota, Miguel José Mascarenhas Saraiva, João Afonso Tiago Ribeiro, Pedro Cardoso, Miguel Martins, Guilherme Macedo
Institutions: Hospital de São João, Universidade do Porto
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4409791838