ColonoscopyNCT07329816
External, Multicentre Validation of a Machine-Learning Model to Predict Colonic Adenoma in Indian Adults
An observational study of Colonoscopy, run by Asian Institute of Gastroenterology, India.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Colonoscopy
- What is being tested?
- Not Applicable / Observational study
- Type of treatment
- Surgery or procedure
- How many people
- 1,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Feb 2026to 30 Mar 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 1000
Who may be able to join
- 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.
Who cannot join
- • 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.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: External, Multicentre Validation of a Machine-Learning Model to Predict Colonic Adenoma in Indian Adults-A Prospective, Observational, Multicentre Study
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.
- Conditions
- Colonoscopy
- Being tested
- Not Applicable / Observational study
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Not Applicable / Observational studyProcedure or surgery
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 group | Role | What they receive |
|---|---|---|
| Single prospective observational cohort | Participants undergo standard-of-care colonoscopy
No allocation into treatment or comparison arms Receives: Procedure: Not Applicable / Observational study |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 1 YEAR
Secondary outcomes (1)
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.
Measured: 1 YEAR
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Jan 2026
- Study start1 Feb 2026 (estimated)
- Primary completion30 Mar 2027 (estimated)
- Study completion30 Mar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for ColonoscopyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Asian Institute of Gastroenterology, India
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Mohan Ramchandani, Asian Institute of Gastroenterology, India
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07329816
- Sponsor's protocol ID
- VALID-ADENOMA-IN
- Status last verified
- Jan 2026
- First submitted
- 26 Dec 2025
- Last updated
- 12 Jan 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- External, Multicentre Validation of a Machine-Learning Model to Predict Colonic Adenoma in Indian Adults-A Prospective, Observational, Multicentre Study
- ClinicalTrials.gov ID
- NCT07329816
- Acronym / sponsor ID
- VALID-ADENOMA-IN
- Conditions
- Colonoscopy
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Asian Institute of Gastroenterology, IndiaUniversity, hospital or other
- Study start
- 1 Feb 2026 (estimated)
- Primary completion
- 30 Mar 2027 (estimated)
- Study completion
- 30 Mar 2027 (estimated)
- First posted
- 9 Jan 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Single prospective observational cohort | – | Procedure: Not Applicable / Observational studyParticipants undergo standard-of-care colonoscopy No allocation into treatment or comparison arms |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 1000
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (3 listed)
- • Known CRC or polyp, prior colectomy, polyposis syndromes, known IBD, or strong hereditary CRC syndromes (e.g., Lynch) if excluded in derivation.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Not Applicable / Observational studyNo 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. | Procedure or surgery | Single prospective observational cohort |
Primary outcome
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
1 secondary outcome
Validation Performance of the Machine Learning Prediction Model
Time frame: 1 YEAR
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Jan 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
An observational study of Colonoscopy, run by Asian Institute of Gastroenterology, India.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 12 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact DR. NITIN JAGTAP, MD,DM (8712015028, docsnitin13@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Receiver Operating Characteristic Curve (AUROC) of the Machine Learning Model (time frame: 1 YEAR). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Jan 2026.