Artificial Intelligence-driven Tuberculosis Landscape Analysis & Stratification Research
China31,600 participantsStarted 2026-06-01
Plain-language summary
The goal of this observational study is to establish and validate a comprehensive AI-driven clinical decision support system (AI-CDSS) in whole-chain management for pulmonary tuberculosis (TB) patients. The main question it aims to answer is:
How is the predictive performance of this system in terms of multiple key links during TB diagnosis and treatment? Can real-world benefits be derived from this system? This AI framework supports clinicians in making smarter decisions, ultimately improving cure rates and ensuring that every patient receives the most effective, personalized care possible.
Who can participate
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See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Inclusion Criteria for Model Development Cohort:
* Patient with clinically diagnosed or bacteriologically confirmed pulmonary tuberculosis (TB) who received TB treatment;
* Initiation of TB treatment on or after January 1, 2021;
* Complete key diagnosis and treatment data available in the electronic medical record system.
Inclusion Criteria for External Validation Cohort:
* Patient with clinically diagnosed or bacteriologically confirmed pulmonary tuberculosis (TB) who is planning to start TB treatment;
* Voluntary participation with signed informed consent form (for adults ≥18 years); parental / guardian consent and co-signed informed consent form are required for minors aged ≤ 18 years.
Exclusion Criteria:
* Co-morbidity confounding: the presence of other active, life-threatening disease (e.g. late-stage malignancy, non-HIV severe immunodeficiency) for which the expected survival or priority of treatment may substantially interfere with the attribution of TB treatment outcomes;
* Extremely poor treatment adherence: documented evidence indicating that the patient either never initiated treatment or was permanently lost to follow-up within the early treatment period (\<2 weeks), precluding the collection of any valid outcome data.
Questions worth asking your doctor
Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.
1Based on my diagnosis and history, is this trial worth exploring for me — or is there a standard treatment we should try first?
2What does this trial's phase tell us about how much is already known about its safety and benefit?
3What would taking part actually involve for me — visits, tests, time, and travel?
4What are the known and possible risks or side effects I should weigh, and how would they be monitored?
5If this trial isn't the right fit, what other options or trials would you suggest I look into?
Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
Questions for the trial coordinator
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
1What does taking part actually involve week to week — how many visits, where, and how long does each one take?
2What costs are covered by the study, and what might I have to pay for myself, including travel, parking, or time off work?
3What happens during screening, and what happens if the study team confirms I don't meet the criteria after those tests?
4Who pays for the scans, blood work, and other tests the trial requires — the study, my insurance, or me?
5How will being in the trial affect my regular care, and will my own doctor stay informed and involved?
6Can I leave the trial at any point if I change my mind, and what would happen to my care if I do?
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
What they're measuring
1
Predictive Performance of the "Easy-to-Treat" versus "Hard-to-Treat" stratification model for pulmonary tuberculosis (PTB)
Timeframe: from treatment initiation to 6 months post treatment