Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
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.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Single prospective observational cohort | Participants undergo standard-of-care colonoscopy No allocation into treatment or comparison arms |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 | 1 YEAR |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 1 YEAR |
Not provided
Inclusion Criteria:
Exclusion Criteria:
• Known CRC or polyp, prior colectomy, polyposis syndromes, known IBD, or strong hereditary CRC syndromes (e.g., Lynch) if excluded in derivation.
Not provided
Not provided
1000
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| DR. NITIN JAGTAP, MD,DM | Contact | 8712015028 | docsnitin13@gmail.com | |
| DR NITIN JAGTAP, MD,DM | Contact | 8712015028 | docsnitin13@gmail.com |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D019370 | Observation |
| ID | Term |
|---|---|
| D008722 | Methods |
| D008919 | Investigative Techniques |
Not provided
Not provided
Not provided
Not provided
Not provided
|