Artificial Intelligence-driven Tuberculosis Landscape Analysis & Stratification Research

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.

• Brier Score of the "Easy-to-treat" versus "Hard-to-treat" Model [Срок оценки: 6 months post-treatment]
• Calibration Slope of the "Easy-to-treat" versus "Hard-to-treat" Model [Срок оценки: 6 months post-treatment]
• Area Under the Receiver Operating Characteristic (AUROC) Curve of the Pre-Drug Susceptibility Testing (Pre-DST) Drug Resistance Predictive Model [Срок оценки: 6 months post-treatment]
• Area Under the Precision-Recall Curve (AUPRC) of the Pre-Drug Susceptibility Testing (Pre-DST) Drug Resistance Predictive Model [Срок оценки: 6 months post-treatment]
• F1-score of the Secondary Decision Models for Pre-Drug Susceptibility Testing (Pre-DST) Drug Resistance Prediction [Срок оценки: 6 months post-treatment]
• Area Under the Receiver Operating Characteristic (AUROC) Curve of the Adherence Forecasting Model [Срок оценки: From treatment initiation until treatment completion, assessed up to 6 months]
• Area Under the Precision-Recall Curve (AUPRC) of the Adherence Forecasting Model [Срок оценки: From treatment initiation until treatment completion, assessed up to 6 months]
• F1-score of the Secondary Decision Models for Adherence Forecasting [Срок оценки: From treatment initiation until treatment completion, assessed up to 6 months]
• Area Under the Receiver Operating Characteristic (AUROC) Curve of the Treatment Response Predictive Model [Срок оценки: 6 months post-treatment]
• Area Under the Precision-Recall Curve (AUPRC) of the Treatment Response Predictive Model [Срок оценки: 6 months post-treatment]
• F1-score of the Secondary Decision Models for Treatment Response Prediction [Срок оценки: 6 months post-treatment]
• Area Under the Receiver Operating Characteristic (AUROC) Curve of the Adverse Event (AE) Predictive Model [Срок оценки: 6 months post-treatment]