The Histological Detection of Ulcerative Colitis Using a No-Code Artificial Intelligence Model
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Updated: Aug 26, 2026
Flexible Colonoscopy in Mice to Evaluate the Severity of Colitis and Colorectal Tumors Using a Validated Endoscopic Scoring System
Published on: October 16, 2013
Yuichiro Hamamoto1,2, Michihiro Kawamura3, Hiroki Uchida3
1Department of Diagnostic Pathology, Kinki Central Hospital of Mutual Aid Association of Public School Teachers, Itami, Hyogo, Japan.
Abstract:
Ulcerative colitis (UC) is an intractable disease that affects young adults. Histological findings are essential for its diagnosis; however, the number of diagnostic pathologists is limited. Herein, we used a no-code artificial intelligence (AI) platform "Teachable Machine" to train a model that could distinguish between histological images of UC, non-UC coloproctitis, adenocarcinoma, and control. A total of 5100 histological images for training and 900 histological images for testing were prepared by pathologists. Our model showed accuracies of 0.99, 1.00, 0.99, and 0.99, for UC, non-UC coloproctitis, adenocarcinoma, and control, respectively. This is the first report in which a no-code easy AI platform has been able to comprehensively recognize the distinctive histologic patterns of UC.
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