  Artificial intelligence (AI) products can be trained to recognize
tuberculosis (TB)-related abnormalities on chest radiographs. Various AI
products are available commercially, yet there is lack of evidence on how their
performance compared with each other and with radiologists. We evaluated five
AI software products for screening and triaging TB using a large dataset that
had not been used to train any commercial AI products. Individuals (>=15 years
old) presenting to three TB screening centers in Dhaka, Bangladesh, were
recruited consecutively. All CXR were read independently by a group of three
Bangladeshi registered radiologists and five commercial AI products: CAD4TB
(v7), InferReadDR (v2), Lunit INSIGHT CXR (v4.9.0), JF CXR-1 (v2), and qXR
(v3). All five AI products significantly outperformed the Bangladeshi
radiologists. The areas under the receiver operating characteristic curve are
qXR: 90.81% (95% CI:90.33-91.29%), CAD4TB: 90.34% (95% CI:89.81-90.87), Lunit
INSIGHT CXR: 88.61% (95% CI:88.03%-89.20%), InferReadDR: 84.90% (95% CI:
84.27-85.54%) and JF CXR-1: 84.89% (95% CI:84.26-85.53%). Only qXR met the TPP
with 74.3% specificity at 90% sensitivity. Five AI algorithms can reduce the
number of Xpert tests required by 50%, while maintaining a sensitivity above
90%. All AI algorithms performed worse among the older age and people with
prior TB history. AI products can be highly accurate and useful screening and
triage tools for TB detection in high burden regions and outperform human
readers.
