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AI could help predict colon cancer risk before a colonoscopy

NCT ID NCT07329816

What the study statuses mean

This study's is highlighted.

Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting This study
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Jun 27, 2026 · Last updated Jun 27, 2026

Summary

This study will test whether a computer model can accurately predict the chance of finding precancerous colon polyps in 1,000 Indian adults undergoing a routine colonoscopy. The model uses basic health information like age, sex, and lifestyle factors. If it works well, it could help doctors decide who most needs a colonoscopy, saving time and resources.

What this could mean

Our plain-language read of the trial. This is informational only, not medical advice or a prediction.

What this could lead to
If successful, this could lead to a simple, low-cost tool to better identify who needs a colonoscopy, potentially catching more precancerous polyps early.
What could go wrong
This is an early validation study, not a treatment trial. The model may not perform well enough in real-world settings, and it won't directly improve health outcomes on its own.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Participants

About 1,000 people

The number the study aims to enrol. It can still change while the study runs.

Expected to start

Feb 2026

An estimate. Start dates often move.

Expected to finish

Mar 2027

An estimate. End dates often move.

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

1000

Ages

18 to 75 years

Sex

Anyone

Healthy volunteers

Not accepted

This study is not open to healthy volunteers. The entry requirements below say who it is open to.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

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.

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Conditions

The condition(s) this trial relates to.

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

How to take part

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  1. The official record

    ClinicalTrials.gov lists the study team's own contact details, including names and phone numbers. We don't republish those.

    Open the record ↗

  2. A doctor treating you

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