Crohn's disease (CD) is an incurable inflammatory bowel disease that can lead to a variety of complications and requires lifelong treatment. However, the diagnosis and management of Crohn's disease exhibit high rates of misdiagnosis and missed diagnoses, along with significant variability, among primary care facilities and novice endoscopists. Therefore, we established an interpretable artificial intelligence (AI) system using double‐balloon enteroscopy to facilitate Crohn's disease ulcer identification and grading.

To develop an interpretable AI system for the identification and grading of Crohn's disease ulcer images, offering bounding box localization for visual interpretability and factor‐specific grading explanations for each ulcer to improve assessment performance.

We constructed a region and grading model of individual ulcers based on the YOLO‐v5 algorithm. By analyzing …