A research toolkit to support the effective use of computer-aided detection (CAD) software for TB by calibrating CAD score thresholds and other parameters. 

Chest radiography (CXR) plays a key role in the screening and triage of pulmonary tuberculosis (TB) and can guide the effective use of diagnostic testing to improve case detection and cost-efficiency.  Computer-aided detection (CAD) products use artificial intelligence (AI) to analyse CXR for the presence of abnormalities suggestive of pulmonary TB and can improve the feasibility and performance of CXR for TB screening and triage.  CAD technologies for TB detection have recently been recommended for use by WHO among adults aged 15 years or more, in place of human readers for interpretation of digital chest radiography in both screening and triage for TB disease.

CAD products produce an abnormality score that can be used to signal probable TB and trigger further TB diagnostic evaluation relative to a selected threshold. Identifying an appropriate threshold requires the calibration of CAD products based on the local context and intended use, as well as decision making around the goals for screening and acceptable costs.