Improving the Real-time Classification of Disease Severity in Ulcerative Colitis: Artificial Intelligence as the Trigger for a Second Opinion

Objective: Endoscopic classification of ulcerative colitis (UC) shows high interobserver variation. Previous research demonstrated that artificial intelligence (AI) can match the accuracy of central reading in scoring still images. We now extend this assessment to longer colon segments and integrate AI into clinical workflows, evaluating its use for real-time, video-based classification of disease severity, and as a support system for physicians. Methods: We trained a convolutional neural network with the Mayo Endoscopic Subscores (MES) of 2,561 images and 53 videos from 645 patients. The model differentiated scoreable from unscoreable endoscopy sections through open-set recognition. Validation involved 140 video clips from 44 UC patients. Six inflammatory bowel disease (IBD) experts and 16 non-experts rated these videos, with expert scores as the gold standard. We assessed the model’s performance and the value as a supporting system. Lastly, the model underwent an alpha test on a real-world patient as a real-time endoscopic support. Results: The model achieved an accuracy of 82%, with no significant differences between the experts and the AI. When used as a supporting system, it improved non-IBD experts' performance by 12% and disagreed with the primary physician in 20-39% of cases. During the alpha test, it was successfully integrated into clinical practice, accurately distinguishing between MES 0 and MES 1, consistent with endoscopists' assessments. Conclusions: Our innovative AI model shows significant potential for enhancing the accuracy of UC severity classification and improving the proficiency of non-IBD experts. It is designed for clinical use and has proven feasible in real-world testing.