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Abstract
There have been few independent evaluations of computer-aided detection (CAD) software for tuberculosis (TB) screening, despite the rapidly expanding array of available CAD solutions. We developed a test library of chest X-ray (CXR) images which was blindly re-read by two TB clinicians with different levels of experience and then processed by 12 CAD software solutions. Using Xpert MTB/RIF results as the reference standard, we compared the performance characteristics of each CAD software against both an Expert and Intermediate Reader, using cut-off thresholds which were selected to match the sensitivity of each human reader. Six CAD systems performed on par with the Expert Reader (Qure.ai, DeepTek, Delft Imaging, JF Healthcare, OXIPIT, and Lunit) and one additional software (Infervision) performed on par with the Intermediate Reader only. Qure.ai, Delft Imaging and Lunit were the only software to perform significantly better than the Intermediate Reader. The majority of these CAD software showed significantly lower performance among participants with a past history of TB. The radiography equipment used to capture the CXR image was also shown to affect performance for some CAD software. TB program implementers now have a wide selection of quality CAD software solutions to utilize in their CXR screening initiatives.
Highlights
There have been few independent evaluations of computer-aided detection (CAD) software for tuberculosis (TB) screening, despite the rapidly expanding array of available CAD solutions
Several large-scale, community-based chest X-ray (CXR) screening initiatives have been recently implemented in high TB burden countries[6,7,8,9,10,11]
Three CAD software solutions emerged from this evaluation as excellent alternatives for human CXR interpretation, performing on par with the Expert Reader and significantly better than the Intermediate Reader: Qure.ai qXR v3, Delft Imaging CAD4TB v7 and Lunit INSIGHT CXR v3.1.0.0
Summary
There have been few independent evaluations of computer-aided detection (CAD) software for tuberculosis (TB) screening, despite the rapidly expanding array of available CAD solutions. Several large-scale, community-based CXR screening initiatives have been recently implemented in high TB burden countries[6,7,8,9,10,11] These programs show that this case finding approach is both feasible to implement in low- and middle-income countries (LMIC) and effective at identifying people with TB, those with subclinical disease who are frequently missed by TB programs or only diagnosed after long delays[12]. These scores can be dichotomized at a selected threshold, above which the CXR image is categorized as abnormal and the individual is indicated for further TB evaluations, such as a sputum-based molecular diagnostic test. The AI algorithms in some CAD solutions will automatically select a cut-off threshold for users, and will continuously use follow-on sputum test result data to optimize threshold selection
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