Artificial Intelligence-driven Tuberculosis Landscape Analysis & Stratification Research

Artificial Intelligence-driven Tuberculosis Landscape Analysis & Stratification Research

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.

This study establishes TB-ATLAS (Artificial Intelligence-driven Tuberculosis Landscape Analysis & Stratification Research), a modular framework for whole-chain TB management. The objective is to develop and validate an umbrella suite of AI-driven models to optimize clinical decision-making from initial diagnosis to post-treatment follow-up.

The core hypothesis is that multimodal patient data can stratify TB phenotypes and predict critical clinical events, enabling precision medicine. Beyond the primary focus on distinguishing Easy-to-Treat (ETT) from Hard-to-Treat (HTT) categories, the system incorporates satellite modules for pre-DST drug resistance risk, treatment adherence monitoring, adverse event (AE) early warning, and risk of post-TB lung disease (PTLD).

This study employs a retrospective-prospective cohort design. By utilizing retrospective IPD from clinical trials and real-world EHRs (>30,000 patients), the investigators apply advanced AI, including foundation models for feature representation and multi-task learning for modular development. Integration of structured clinical variables, microbiological profiles, radiomics, and host signatures ensures high-dimensional input. Model interpretability is prioritized via SHAP/LIME to ensure clinical trust. Then the performance will be evaluated using AUROC and calibration metrics. External validation will occur in a prospective cohort (n≥1,600) to assess the system's impact on predicting real-world outcomes compared to standardized care.

The expected output is the TB-ATLAS Clinical Decision Support System (AI-CDSS). By providing evidence-based guidance on regimen intensity, resistance risk, and relapse monitoring, this platform facilitates the transition from "one-size-fits-all" standardized care towards individualized precision management, significantly enhancing clinical decision-making across diverse healthcare settings.

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.