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

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

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07329816

Enrollment

1000

Registered

2026-01-09

Start date

2026-02-01

Completion date

2027-03-30

Last updated

2026-01-12

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Colonoscopy

Brief 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.

Interventions

PROCEDURENot 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.

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 75 Years

Healthy volunteers

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.

Design outcomes

Primary

Measure	Time frame	Description
Area Under the Receiver Operating Characteristic Curve (AUROC) of the Machine Learning Model	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

Measure	Time frame	Description
Validation Performance of the Machine Learning Prediction Model	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.

Contacts

Primary ContactDR. NITIN JAGTAP, MD,DM

docsnitin13@gmail.com8712015028

Backup ContactDR NITIN JAGTAP, MD,DM