Background and aims: Image-enhanced endoscopy (IEE) has attracted attention as a method for detecting inflammation and predicting outcomes in patients with ulcerative colitis (UC); however, the procedure requires specialist endoscopists. Artificial intelligence (AI)-assisted IEE may help non-experts to provide objective accurate predictions using 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 histological assessment were recorded for six 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=0.01). In a sub-analysis predicting clinical relapse in patients with MES ≤1, the area under the 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.
Keywords: Ulcerative colitis; artificial intelligence; colonoscopy.
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