Last reviewed · How we verify

NCT07329816

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

Not yet recruiting Last updated 12 January 2026
What this trial tests

trial testing Not Applicable / Observational study in Colonoscopy in 1,000 participants. Not yet recruiting.

Timeline
1 February 2026
Primary endpoint
30 March 2027
30 March 2027

Quick facts

Lead sponsorAsian Institute of Gastroenterology, India
StatusNot yet recruiting
Study typeOBSERVATIONAL
Enrollment1,000
Start date1 February 2026
Primary completion30 March 2027
Estimated completion30 March 2027

Drugs / interventions tested

Conditions studied

Sponsor

Asian Institute of Gastroenterology, India

Who can join

Adults 18 to 75, any sex, with Colonoscopy. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.

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Other recruiting trials for Colonoscopy

Currently open trials in the same condition.

Other Asian Institute of Gastroenterology, India trials

Trials by the same sponsor.

Verify against primary sources

Data sources for this page

Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT07329816.

Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing