Colorectal adenomas are precursors to colorectal cancer (CRC). Accurate pre-procedure risk stratification could optimize colonoscopy yield and resource allocation in India, where adenoma prevalence varies by age, sex, and lifestyle/metabolic factors. ML models can integrate multiple predictors to estimate individualized risk. Existing risk scores are largely Western; performance and calibration may not be appropriate in Indian populations with different socio-demographic and metabolic profiles. External, prospective, multicentre validation is essential before clinical implementation.
Study Type
OBSERVATIONAL
Enrollment
1,000
No study-specific intervention is administered. Participants undergo standard-of-care diagnostic colonoscopy and histopathological evaluation. A locked machine-learning model is applied to routinely collected baseline clinical and demographic data for risk prediction only, without influencing clinical management.
Area Under the Receiver Operating Characteristic Curve (AUROC) of the Machine Learning Model
Area under the receiver operating characteristic curve (AUROC) of the machine learning-based prediction model for identifying the presence of histologically proven colonic adenoma
Time frame: 1 YEAR
Validation Performance of the Machine Learning Prediction Model
Validation performance of the machine learning model for predicting colonic adenoma, assessed using AUROC, calibration metrics (Brier score), and calibration plots in an independent validation cohort.
Time frame: 1 YEAR
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