Protocol outlines AI/ML approaches to identify TB transmission drivers and treatment failure factors in Uganda.

This document is a study protocol detailing a planned research initiative focused on Tuberculosis and HIV/TB coinfection. The proposed study will be conducted in Kampala city, Uganda, targeting TB patients and their contacts. The intervention involves the application of health data science approaches, specifically AI/ML algorithms, aimed at identifying factors driving TB transmission and reasons for anti-TB treatment failure. No comparator or specific medication interventions are defined in this protocol.

The protocol outlines several secondary outcomes intended for evaluation. These include identifying patients at baseline who are unlikely to convert their sputum or culture results by months 2 and 5. Additionally, the study aims to identify household contacts of TB cases who are at risk of developing TB disease and to identify contacts who may resist TB infection despite repeated exposure to M. tuberculosis. The follow-up period for these assessments is set at months 2 and 5.

Critical details regarding the study design remain undefined in this protocol. The sample size is not reported, and the primary outcome is not reported. Safety data, including adverse events, serious adverse events, discontinuations, and tolerability, are not reported as the study has not yet commenced. Funding sources and potential conflicts of interest are not reported. Consequently, the certainty of any future findings cannot be assessed at this stage, and no causal conclusions can be drawn from this planning document.

Practice relevance is not reported for this protocol. Clinicians should note that this document represents a research plan rather than completed evidence. Until the study is executed and results are published, the proposed use of AI/ML algorithms in this setting remains a hypothesis rather than a validated strategy for clinical management.