| 基于深度学习的计算机辅助诊断系统在肋骨骨折诊断中的应用 |
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| 引用本文： | 熊山,陈博,毛杰,刘四斌,黄原义,程建敏.基于深度学习的计算机辅助诊断系统在肋骨骨折诊断中的应用[J].CT理论与应用研究,2022,31(5):617-622. |
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| 作者姓名： | 熊山 陈博 毛杰 刘四斌 黄原义 程建敏 |
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| 作者单位： | 1.荆州市中心医院放射科, 湖北 荆州434020 |
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| 基金项目： | 荆州市科技局指令性项目（2021CC28-06）。 |
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| 摘 要： | 目的:探讨基于深度学习（DL）的计算机辅助诊断（CAD）系统在肋骨骨折诊断中的应用价值。方法:回顾性分析232例胸部外伤患者CT图像并采用3种方式阅片。CAD系统阅片:应用CAD系统行肋骨骨折检测并记录结果；医师阅片:两名具有6年肋骨骨折CT诊断经验放射科主治医师独立阅片并以两人一致意见作为诊断结果；CAD系统辅助医师阅片:CAD系统辅助两名医师采用共同阅片模式阅片。金标准:两名具有15年以上肋骨骨折CT诊断经验的放射科高年资医师对患者初诊及复诊CT独立阅片，结果不一致时以两人协商一致的意见作为肋骨骨折诊断金标准。计算并比较3种阅片方式的敏感度、假阳性率及阅片时间。结果:232例患者共发现712处肋骨骨折。CAD系统阅片敏感度为81.2％，CAD系统阅片敏感度低于医师，医师阅片敏感度低于CAD系统辅助医师阅片。CAD系统阅片假阳性率为0.48±0.13，在3种方式中最高，医师阅片与CAD系统辅助医师阅片假阳性率差异无统计学意义。CAD系统阅片时间为（2.45±0.92）s，在3种方式中耗时最少，CAD系统辅助医师阅片时间少于医师阅片且阅片时间减少34.2％。结论:进一步提高敏感度并降低假阳性率是CAD改进的重要内容；采用基于深度学习的CAD系统辅助医师阅片可在不增高假阳性率的同时提高敏感度和减少阅片时间。
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| 关 键 词： | 深度学习 卷积神经网络 体层摄影术 X线计算机 肋骨骨折 |
| 收稿时间： | 2021-10-10 |
Application of Computer-aided Diagnosis System Based on Deep Learning in Rib Fracture Diagnosis |
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| Institution: | 1.Department of Radiology, Jingzhou Central Hospital, Jingzhou 434020, China2.Department of Radiology, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China3.Department of Radiology, Dongyang People's Hospital, Jinhua 322100, China |
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| Abstract: | Objective: To investigate the application value of computer-aided diagnosis (CAD) system based on deep learning (DL) in rib fracture diagnosis. Methods: The CT images of 232 patients with chest trauma were analyzed retrospectively and the films were read in three ways. CAD system reading: using CAD system to detect and record the results of rib fracture; radiologists reading: two radiologists with 6 years of CT diagnosis experience read the film independently and the diagnostic results were based on the consensus of them; radiologists reading with the assistance of CAD system: one month later, the same two radiologists reassessed the images with the aid of the CAD system using a joint reading mode. Gold standard: two senior radiologists with more than 15 years of experience in the CT diagnosis of rib fractures read the radiographs independently and the consensus of them was used as the diagnostic standard. The sensitivity, false-positive rate and the reading time of the three methods were calculated and compared. Results: A total of 712 rib fractures were found in 232 patients. The reading sensitivity of the CAD system was 81.2%, which was lower than that of the radiologists, and the reading sensitivity of the radiologists was lower than that of CAD system-assisted radiologists. The false positive rate of CAD system was 0.48±0.13 and was the highest . There was no statistical difference in the false-positive rate between radiologists and CAD system-assisted radiologists. The reading time of the CAD system was (2.45±0.92)s and was the shortest. The reading time of CAD system-assisted radiologists was less than that of radiologists and the reading time was reduced by 34.2%. Conclusion: To further improve the sensitivity and reduce the false positive rate is an important part of CAD improvement; the use of CAD system based on deep learning to assist radiologists in reading images can improve the sensitivity of rib fracture diagnosis and reduce the time of reading images without increasing the false positive rate. |
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