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.

该研究基于深度学习开发了自动检测算法，可在胸部 X 光上检测和定位七种特定的放射学发现，这种算法在辅助 junior 和 intermediate 放射科医师判断的准确性上有潜在的医疗应用价值。

深度学习能够可靠地识别胸部 X 光异常模式吗？一项多读者研究检查 AI 在日常放射学临床实践中一个月的实施

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