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In this study, we developed a deep-learning-based automatic detectionalgorithm (DLAD, Carebot AI CXR) to detect and localize seven specificradiological findings (atelectasis (ATE), consolidation (CON), pleural effusion(EFF), pulmonary lesion (LES), subcutaneous emphysema (SCE), cardiomegaly(CMG), pneumothorax (PNO)) on chest X-rays (CXR). We collected 956 CXRs andcompared the performance of the DLAD with that of six individual radiologistswho assessed the images in a hospital setting. The proposed DLAD achieved highsensitivity (ATE 1.000 (0.624-1.000), CON 0.864 (0.671-0.956), EFF 0.953(0.887-0.983), LES 0.905 (0.715-0.978), SCE 1.000 (0.366-1.000), CMG 0.837(0.711-0.917), PNO 0.875 (0.538-0.986)), even when compared to the radiologists(LOWEST: ATE 0.000 (0.000-0.376), CON 0.182 (0.070-0.382), EFF 0.400(0.302-0.506), LES 0.238 (0.103-0.448), SCE 0.000 (0.000-0.634), CMG 0.347(0.228-0.486), PNO 0.375 (0.134-0.691), HIGHEST: ATE 1.000 (0.624-1.000), CON0.864 (0.671-0.956), EFF 0.953 (0.887-0.983), LES 0.667 (0.456-0.830), SCE1.000 (0.366-1.000), CMG 0.980 (0.896-0.999), PNO 0.875 (0.538-0.986)). Thefindings of the study demonstrate that the suggested DLAD holds potential forintegration into everyday clinical practice as a decision support system,effectively mitigating the false negative rate associated with junior andintermediate radiologists.

Can Deep Learning Reliably Recognize Abnormality Patterns on Chest  X-rays? A Multi-Reader Study Examining One Month of AI Implementation in  Everyday Radiology Clinical Practice