Artificial intelligence–driven prioritization of colonoscopy referrals using multicenter clinical data: addressing imbalanced and incomplete data

Long wait times for colonoscopy and challenges in referral prioritization contribute to procedural backlogs in endoscopy units, potentially delaying the diagnosis of colorectal cancer (CRC) and other clinically significant conditions. In Québec, colonoscopy referrals are currently prioritized using structured clinical indications documented in the AH-702 referral form. This study aimed to develop and externally validate an explainable artificial intelligence (AI) framework for predicting colonoscopy referral priorities across multiple healthcare centers while addressing missing data and class imbalance. This multicenter retrospective study included 11,680 patients referred for colonoscopy at two tertiary centers in Québec, Canada, between 2018 and 2022. Using 135 clinical, demographic, diagnostic, and pathological features, a multiclass decision-tree model was trained with advanced imputation and synthetic data generation. The model was evaluated on an internal held-out test set and an independent external cohort of 2,977 patients. The optimized model achieved an area under the receiver operating characteristic curve (AUROC) of 98.4% and a balanced accuracy of 95.7% on the internal held-out test set. External evaluation yielded an AUROC of 97.9% and a balanced accuracy of 96.4%. When the 22 structured AH-702 indication fields were withheld, balanced accuracy decreased to 28.1% internally and 28.5% externally, while AUROC decreased to 60.1% and 61.3%, respectively. These findings demonstrate that the model identified clinically relevant referral indications as key predictors, consistent with the existing AH-702 prioritization framework. This interpretable AI framework demonstrated strong retrospective performance for predicting colonoscopy referral priorities across three healthcare centers. The findings support further investigation of its potential as a decision-support tool for referral prioritization, pending prospective validation.