Artificial Intelligence-driven Tuberculosis Landscape Analysis & Stratification Research
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorHuashan Hospital
About this trial
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.
Eligibility criteria
Qualifiers
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.
Patient with clinically diagnosed or bacteriologically confirmed pulmonary tuberculosis (TB) who is planning to start TB treatment;
Disqualifiers
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.
Trial design
Treatments tested in this trial
- Not listed
Trial groups
31,600 Participants
are grouped into 2 trial groupsSponsors and collaborators
Huashan Hospital
Lead sponsor
The Hong Kong Polytechnic University
Collaborator
Source ClinicalTrials.gov