Ancient wisdom meets AI: could TCM features sharpen colorectal screening?
NCT ID NCT07784114
First seen Aug 25, 2026 · Last updated Aug 26, 2026 · Updated 1 time
Summary
This study tests whether adding Traditional Chinese Medicine (TCM) features to standard Western medical data improves machine-learning models that predict colorectal conditions before a colonoscopy. Researchers will compare two strategies: one using only Western clinical and lab data, and another that also includes structured TCM features. The models aim to first distinguish functional bowel issues from inflammatory or neoplastic diseases, then, in higher-risk cases, identify ulcerative colitis. Using data from 1,500 adults across multiple centers, the study will see if the TCM-integrated approach is more accurate and clinically useful.
What this could mean
Our plain-language read of the trial. This is informational only, not medical advice or a prediction.
- Active substance
- Traditional Chinese Medicine (TCM) clinical features integrated into machine-learning diagnostic models
- What this could lead to
- If successful, this could lead to better pre-colonoscopy risk assessment, helping doctors identify who needs a colonoscopy most urgently.
- What could go wrong
- This is an observational study, not a treatment trial. The models may not perform better than Western-only approaches, and results won't replace colonoscopy or clinical judgment.
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
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1,500 people
The number who actually took part.
- Started
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Jan 2015
- Finished
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Jun 2025
- Lead sponsor
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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
This study includes adults from multiple participating centers in China who underwent clinical evaluation and colonoscopy-related diagnostic work-up for suspected functional bowel disorders or colorectal inflammatory / neoplastic disease. A development cohort is used to train two-stage machine-learning prediction models under Western-only and TCM-integrated strategies. Independent external-validation cohorts from other centers are used to assess generalizability. No study treatment is assigned; analyses use existing clinical data collected under institutional approvals.
- Ages
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18 to 85 years
- Sex
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Anyone
- Healthy volunteers
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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 Hide 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 aged 18 years or older Patients evaluated in participating centers with clinical records suitable for pre-endoscopic risk assessment related to colorectal inflammatory or functional bowel conditions Underwent colonoscopy (or had a colonoscopy-confirmed final diagnosis) allowing assignment to the study diagnostic labels used in the two-stage framework Stage 1-eligible diagnoses classifiable as either a functional-predominant pathway (e.g., irritable bowel syndrome / related functional presentations as defined in the protocol) or an inflammatory-neoplastic pathway (ulcerative colitis, Crohn disease, indeterminate colitis, or colorectal neoplasia as defined in the protocol) For Stage 2 analyses: patients within the inflammatory-neoplastic pathway classifiable as ulcerative colitis versus other inflammatory or neoplastic conditions (Crohn disease, indeterminate colitis, or colorectal neoplasia) Availability of required Western clinical and laboratory predictors for model development or validation For TCM-integrated analyses: availability of protocol-defined structured Traditional Chinese Medicine (TCM) features Ability to assign the record to the development cohort or an independent external-validation cohort according to the protocol center rules Exclusion Criteria: Age younger than 18 years Missing or unverifiable final diagnostic label required for Stage 1 and/or Stage 2 Missing key predictors required by the Western-only and/or TCM-integrated modeling pipelines (per protocol variable lists) Diagnosis outside the predefined Stage 1 / Stage 2 label sets and not mappable under protocol rules Duplicate or overlapping records for the same patient encounter after de-duplication Records excluded by pre-specified data-quality or center-specific protocol rules (e.g., incomplete critical clinical documentation)
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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.
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