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External, Multicentre Validation of a Machine-Learning Model to Predict Colonic Adenoma in Indian Adults
Sponsor: Asian Institute of Gastroenterology, India
Summary
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.
Official title: External, Multicentre Validation of a Machine-Learning Model to Predict Colonic Adenoma in Indian Adults-A Prospective, Observational, Multicentre Study
Key Details
Gender
All
Age Range
18 Years - 75 Years
Study Type
OBSERVATIONAL
Enrollment
1000
Start Date
2026-02-01
Completion Date
2027-03-30
Last Updated
2026-01-12
Healthy Volunteers
No
Conditions
Interventions
Not Applicable / Observational study
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.