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NCT Number: NCT07329816

External, Multicentre Validation of a Machine-Learning Model to Predict Colonic Adenoma in Indian Adults

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.

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Key information

Conditions

Colonoscopy

Age range

18 year–75 year

Sex eligibility

All sexes

Study type

Observational

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Adults ≥18 years undergoing diagnostic colonoscopy.
  • Adequate bowel preparation (Boston Bowel Preparation Scale total ≥6 with each segment ≥2).
  • Complete examination (cecal intubation; withdrawal time ≥6 min when no therapy).
  • Availability of all model predictors per CRF.

Exclusion criteria

  • • Known CRC or polyp, prior colectomy, polyposis syndromes, known IBD, or strong hereditary CRC syndromes (e.g., Lynch) if excluded in derivation.
  • Inadequate prep, incomplete colonoscopy, obstructing lesions preventing optical diagnosis beyond obstruction.
  • Emergency colonoscopies, therapeutic-only procedures without diagnostic intent.

Treatment and study plan

Not Applicable / Observational study

Procedure

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.

Primary outcomes

  1. Area Under the Receiver Operating Characteristic Curve (AUROC) of the Machine Learning Model

    Time frame: 1 YEAR

    Area under the receiver operating characteristic curve (AUROC) of the machine learning-based prediction model for identifying the presence of histologically proven colonic adenoma

Secondary outcomes

  1. Validation Performance of the Machine Learning Prediction Model

    Time frame: 1 YEAR

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

DR NITIN JAGTAP, MD,DM

CONTACT

[email protected]

8712015028

DR. NITIN JAGTAP, MD,DM

CONTACT

[email protected]

8712015028

Sponsors and collaborators

Lead sponsor

Asian Institute of Gastroenterology, India

Other

Registry information

Official study title

External, Multicentre Validation of a Machine-Learning Model to Predict Colonic Adenoma in Indian Adults-A Prospective, Observational, Multicentre Study

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
Jan 9, 2026
Registry last updated
Jan 12, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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