A novel artificial intelligence-assisted "vascular healing" diagnosis for prediction of future clinical relapse in patients with ulcerative colitis: a prospective cohort study (with video)

Background and Aims: Image -enhanced endoscopy has attracted attention as a method for detecting inflam- mation and predicting outcomes in patients with ulcerative colitis (UC); however, the procedure requires specialist endoscopists. Artificial intelligence (AI)-assisted image -enhanced endoscopy may help nonexperts provide objective accurate predictions with the use of optical imaging. We aimed to develop a novel AI -based system using 8853 images from 167 patients with UC to diagnose "vascular -healing" and establish the role of AI -based vascular -healing for predicting the outcomes of patients with UC. Methods: This open -label prospective cohort study analyzed data for 104 patients with UC in clinical remission. Endoscopists performed colonoscopy using the AI system, which identified the target mucosa as AI -based vascular -active or vascular -healing. Mayo endoscopic subscore (MES), AI outputs, and histologic assessment were recorded for 6 colorectal segments from each patient. Patients were followed up for 12 months. Clinical relapse was defined as a partial Mayo score >2 Results: The clinical relapse rate was significantly higher in the AI -based vascular -active group (23.9% [16/67]) compared with the AI -based vascular -healing group (3.0% [1/33)]; P Z .01). In a subanalysis predicting clinical relapse in patients with MES <1, the area under the receiver operating characteristic curve for the combination of complete endoscopic remission and vascular healing (0.70) was increased compared with that for complete endoscopic remission alone (0.65). Conclusions: AI -based vascular -healing diagnosis system may potentially be used to provide more confidence to physicians to accurately identify patients in remission of UC who would likely relapse rather than remain stable.