Can artificial intelligence (AI) be used to accurately detect tuberculosis (TB) from chest X-rays? An evaluation of five AI products for TB screening and triaging in a high TB burden setting

Abstract

Artificial intelligence (AI) products can be trained to recognizetuberculosis (TB)-related abnormalities on chest radiographs. Various AIproducts are available commercially, yet there is lack of evidence on how theirperformance compared with each other and with radiologists. We evaluated fiveAI software products for screening and triaging TB using a large dataset thathad not been used to train any commercial AI products. Individuals (>=15 yearsold) presenting to three TB screening centers in Dhaka, Bangladesh, wererecruited consecutively. All CXR were read independently by a group of threeBangladeshi 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 Bangladeshiradiologists. The areas under the receiver operating characteristic curve areqXR: 90.81% (95% CI:90.33-91.29%), CAD4TB: 90.34% (95% CI:89.81-90.87), LunitINSIGHT 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 TPPwith 74.3% specificity at 90% sensitivity. Five AI algorithms can reduce thenumber of Xpert tests required by 50%, while maintaining a sensitivity above90%. All AI algorithms performed worse among the older age and people withprior TB history. AI products can be highly accurate and useful screening andtriage tools for TB detection in high burden regions and outperform humanreaders