Powered by artificial intelligence (AI) , particularly deep neural networks, computer aided detection (CAD) tools can be trained to recognize TB-related abnormalities on chest radiographs , thereby screening large numbers of people and reducing the pressure on healthcare professionals. Addressing the lack of studies comparing the performance of different products, we evaluated five AI software platforms specific to TB : CAD4TB ( v6 ), InferReadDR (v2), Lunit INSIGHT for Chest Radiography (v4.9.0), JF CXR-1 (v2) by and qXR (v3) by on an unseen dataset of chest X-rays collected in three TB screening center in Dhaka, Bangladesh. The 23,566 individuals included in the study all received a CXR read by a group of three Bangladeshi board-certified radiologists. A sample of CXRs were re-read by US board-certified radiologists. Xpert was used as the reference standard. All five AI platforms significantly outperformed the human readers . The areas under the receiver operating characteristic curves are qXR: 0.91 (95\% CI: 0.90-0.91), Lunit INSIGHT CXR: 0.89 (95\% CI: 0.88-0.89), InferReadDR: 0.85 (95\% CI: 0.84-0.86), JF CXR-1: 0.85 (95\% CI: 0.84-0.85) , CAD4TB: 0.82 (95\% CI: 0.81-0.83). We also proposed a new analytical framework that evaluates a screening and triage test and informs threshold selection through tradeoff between cost efficiency and ability to triage. Further, we assessed the performance of the five AI algorithms across the subgroups of age , use cases, and prior TB history , and found that the threshold scores performed differently across different subgroups. The positive results of our evaluation indicate that these AI products can be  useful screening and triage tools for active case finding in high TB-burden regions .
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 .
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. Various AI products are available commercially, yet there is lack of evidence on how their performance compared with each other and with radiologists. We
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platforms specific to TB
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
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by on an unseen dataset of chest X-rays collected in three TB screening center in Dhaka, Bangladesh. The 23,566 individuals included in the study all received a CXR read by a group of three Bangladeshi board-certified radiologists. A sample of CXRs were re-read by US board-certified radiologists. Xpert was used as the reference standard.
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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
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, and found that the threshold scores performed differently across different subgroups. The positive results of our evaluation indicate that these
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