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.
<coherence> 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.
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 . 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 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.