Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis

Plain language summaryWhy was this study done?The development of an auxiliary diagnostic tool can reduce the workload of endoscopists and achieve rapid assessment of ulcerative colitis (UC) severity.What did the researchers do?We developed and validated a deep learning-based approach to automatically predict the Mayo endoscopic score using UC endoscopic images.What did the researchers find?The model that was developed in this study achieved high accuracy, fidelity, and stability, and demonstrated potential application in clinical practice.What do the findings mean?Deep learning could effectively assist endoscopists in evaluating the severity of UC in patients using endoscopic images. Background:The ulcerative colitis (UC) Mayo endoscopy score is a useful tool for evaluating the severity of UC in patients in clinical practice. Objectives:We aimed to develop and validate a deep learning-based approach to automatically predict the Mayo endoscopic score using UC endoscopic images. Design:A multicenter, diagnostic retrospective study. Methods:We collected 15120 colonoscopy images of 768 UC patients from two hospitals in China and developed a deep model based on a vision transformer named the UC-former. The performance of the UC-former was compared with that of six endoscopists on the internal test set. Furthermore, multicenter validation from three hospitals was also carried out to evaluate UC-former's generalization performance. Results:On the internal test set, the areas under the curve of Mayo 0, Mayo 1, Mayo 2, and Mayo 3 achieved by the UC-former were 0.998, 0.984, 0.973, and 0.990, respectively. The accuracy (ACC) achieved by the UC-former was 90.8%, which is higher than that achieved by the best senior endoscopist. For three multicenter external validations, the ACC was 82.4%, 85.0%, and 83.6%, respectively. Conclusions:The developed UC-former could achieve high ACC, fidelity, and stability to evaluate the severity of UC, which may provide potential application in clinical practice. Registration:This clinical trial was registered at the ClinicalTrials.gov (trial registration number: NCT05336773)