  In this study, we developed a deep-learning-based automatic detection
algorithm (DLAD, Carebot AI CXR) to detect and localize seven specific
radiological 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 and
compared the performance of the DLAD with that of six individual radiologists
who assessed the images in a hospital setting. The proposed DLAD achieved high
sensitivity (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), CON
0.864 (0.671-0.956), EFF 0.953 (0.887-0.983), LES 0.667 (0.456-0.830), SCE
1.000 (0.366-1.000), CMG 0.980 (0.896-0.999), PNO 0.875 (0.538-0.986)). The
findings of the study demonstrate that the suggested DLAD holds potential for
integration into everyday clinical practice as a decision support system,
effectively mitigating the false negative rate associated with junior and
intermediate radiologists.
