Chest X-ray (CXR) is considered to be the most widely used modality for detecting and monitoring various thoracic findings, including lung carcinoma and other pulmonary lesions. However, X-ray imaging shows particular limitations when detecting primary and secondary tumors and is prone to reading errors due to limited resolution and disagreement between radiologists. To address these issues, we developed a deep-learning-based automatic detection algorithm (DLAD) to automatically detect and localize suspicious lesions on CXRs. Five radiologists were invited to retrospectively evaluate 300 CXR images from a specialized oncology center, and the performance of individual radiologists was subsequently compared with that of DLAD. The proposed DLAD achieved significantly higher sensitivity (0.910 (0.854-0.966)) than that of all assessed radiologists (RAD 10.290 (0.201-0.379), p < 0.001, RAD 20.450 (0.352-0.548), p < 0.001, RAD 30.670 (0.578-0.762), p < 0.001, RAD 40.810 (0.733-0.887), p = 0.025, RAD 50.700 (0.610-0.790), p < 0.001). The DLAD specificity (0.775 (0.717-0.833)) was significantly lower than for all assessed radiologists (RAD 11.000 (0.984-1.000), p < 0.001, RAD 20.970 (0.946-1.000), p < 0.001, RAD 30.980 (0.961-1.000), p < 0.001, RAD 40.975 (0.953-0.997), p < 0.001, RAD 50.995 (0.985-1.000), p < 0.001). The study results demonstrate that the proposed DLAD could be utilized as a decision-support system to reduce radiologists' false negative rate.

Carebot Ltd 128 00 Prague Czech Republic

Department of Artificial Intelligence Daeyang AI Center Sejong University Seoul 050 06 Republic of Korea

Department of Imaging Methods Motol University Hospital 150 06 Prague Czech Republic

Department of Radiodiagnosis Podripska City Hospital 413 01 Roudnice nad Labem Czech Republic

Department of Radiology Masaryk Memorial Cancer Institute 602 00 Brno Czech Republic

Faculty of Electrical Engineering Czech Technical University 166 36 Prague Czech Republic

Faculty of Mathematics and Physics Charles University 121 16 Prague Czech Republic

Faculty of Nuclear Sciences and Physical Engineering Czech Technical University 115 19 Prague Czech Republic

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