Tuberculosis (TB) is a global epidemic and for many years has remained a major cause of death throughout the developing world. Zambia is among the top 30 TB/HIV high burden countries. Chest X-ray (CXR) is recommended as a triaging test for TB, and a diagnostic aid when available. However, many high-burden settings lack access to experienced radiologists capable of interpreting these images, resulting in mixed sensitivity, poor specificity, and large inter-observer variation. In recognition of this challenge, the World Health Organization has recommended the use of automated systems that utilize artificial intelligence (AI) to read CXRs for screening and triaging for TB. In this study, we primarily evaluate the performance of our AI algorithm for TB, and secondarily for Abnormal/Normal.

Inclusion Criteria:

• Participants who are 18 years and older with a known HIV status or are willing to undergo HIV testing if unknown HIV status and meet the following criteria will be included in the study:

  • Presumptive TB patients defined as having any of the following:

    ○ Cough, Weight loss, Night sweats, Fever

  • Household /close TB contacts regardless of symptoms

  • Newly diagnosed HIV regardless of symptoms.

Exclusion Criteria:

• Individuals who do meet the above inclusion criteria will be excluded. In addition, individuals with history of TB treatment within 365 days prior to enrolment will be excluded.

The study will be conducted from Chainda South Clinic, Chawama and Kanyama General Hospitals. These facilities are selected based on existing access to digital radiography. Participants will be drawn from patients attending these health facilities for:

• Health services
• TB households of those who are close contacts of TB households within the catchment area of these facilities