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Published on: October 16, 2013
Automated AI-based Mayo Endoscopic Scoring for ulcerative colitis across adult and pediatric cohorts from diverse
Kamal Hammouda1,2,3, Rishi Dakarapu1, Chathruckan Rajendra4
1Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30322, United States.
Background:
Endoscopic assessment in ulcerative colitis (UC) is critical for clinical decision-making but limited by interobserver variability. AI-powered systems may improve consistency, but dataset heterogeneity has hindered clinical translation. We developed and evaluated a deep learning framework for standardized Mayo Endoscopic Score (MES) prediction across adult and pediatric populations from diverse regions.
Patients And Methods:
Using three multi-institutional datasets, we trained and validated convolutional neural network models to predict MES. The primary dataset (LIMUC; 564 adults, 11 276 images, Turkey) supported model development, with external validation on TMC (308 adults, 7978 images, China) and the Emory Pediatric dataset (EPC; 80 children, 113 images, USA). Two models were developed: UC-Re for binary remission classification (MES 0-1 vs 2-3) and UC-MES for four-class grading (MES 0-3). Imaging artifacts were corrected using inpainting, and Fourier-Spatial Image Harmonization (FSIH) mitigated inter-institutional domain shifts. Performance was evaluated using area under the operating characteristic curve (AUC), F1-score, and quadratic weighted kappa (QWK).
Results:
UC-Re achieved AUCs of 0.98, 0.95, and 0.98 across LIMUC, TMC, and EPC, with F1-scores of 0.92, 0.87, and 0.93, respectively. UC-MES demonstrated strong ordinal consistency (QWK = 0.81-0.85), comparable to inter-expert agreement (QWK = 0.88). Most misclassifications occurred between adjacent MES categories, reflecting human-like patterns.
Conclusions:
This novel AI-based framework predicts MES across geographically diverse adult and pediatric UC datasets. Its strong performance, including comparability to expert gastroenterologists in EPC, supports its potential as a decision-support tool for standardized endoscopic monitoring. However, prospective multicenter validation is warranted prior to routine clinical implementation.
