S1585 Evaluation of the Relationship Between Endoscopic Disease Severity and Treatment Choice for Patients With Ulcerative Colitis Using a Real-World Data Set Linked to Endoscopic Videos Evaluated With Computer Vision

Introduction: Standardized diagnostic tools to assess endoscopic disease severity in patients with ulcerative colitis (UC) are needed but clinical practices may lack appropriate resources for routine deployment. Artificial intelligence (AI) models can enable rapid, standardized assessments of endoscopy videos. This retrospective, observational study assessed whether AI-driven endoscopy findings were associated with treatment plan in patients with UC in community gastroenterology clinics in the United States. Methods: Adults with UC who underwent endoscopy using Iterative Health video recording technology between January 1, 2023 and June 30, 2024 in the Gastro Health network were included. Baseline characteristics and treatment data were collected from electronic health records for 6 months before and 6 months after the date of endoscopy (index date), respectively. Endoscopy videos were analyzed with computer vision AI to generate an AI Endoscopic Score (AI-ES; Mayo endoscopic score model of disease severity [0 = inactive, 1 = mild, 2 = moderate, and 3 = severe]) and AI Endoscopic Score for disease extent (AI-ESe; high is defined as ≥20% of clips showing moderate or severe inflammation). The relationships between AI-ES or AI-ESe and treatment selection were analyzed by logistic regression to calculate odds ratios (ORs) with 95% confidence intervals (CIs), using patients with an AI-ES of 0–1 or a low AI-ESe as a reference. Results: Of 799 patients, 141 (17.6%) had an AI-ES of 2–3 (moderate to severe endoscopic activity) and 158 (19.8%) had extensive disease (high AI-ESe). After endoscopy, 28.4% of patients with an AI-ES of 2–3 received advanced therapy. On univariable analysis, patients with an AI-ES of 2–3 had higher odds of receiving advanced therapy vs no advanced therapy (OR, 1.78; 95% CI, 1.16–2.68) and higher odds of receiving any therapy vs no therapy (OR, 1.70; 95% CI, 1.17–2.46). Patients with a high AI-ESe had higher odds of receiving advanced therapy vs no advanced therapy (OR, 1.82; 95% CI, 1.21–2.71) and higher odds of receiving any therapy vs no therapy (OR, 1.99; 95% CI, 1.39–2.83). Multivariable analysis results were similar. Conclusion: AI-based endoscopic scores correlated with treatment plan for patients with UC. However, disease severity alone may not drive treatment selection and patients with moderate to severe disease may be undertreated in routine care. AI may support standardized treatment decisions and care continuity across providers and experience levels.