Cheng Zhangbo, Zhao Lei, Yan Jun, Zhang Hongbo, Lin Shengmei, Yin Lei, Peng Changli, Ma Xiaohai, Xie Guoxi, Sun Lizhong
Department of Cardiovascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Department of Cardiovascular Surgery, Fujian Provincial Clinical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China.
Quant Imaging Med Surg. 2024 Oct 1;14(10):7365-7378. doi: 10.21037/qims-24-533. Epub 2024 Sep 26.
Aortic dissection is a life-threatening clinical emergency, but it is often missed and misdiagnosed due to the limitations of diagnostic technology. In this study, we developed a deep learning-based algorithm for identifying the true and false lumens in the aorta on non-contrast-enhanced computed tomography (NCE-CT) scans and to ascertain the presence of aortic dissection. Additionally, we compared the diagnostic performance of this algorithm with that of radiologists in detecting aortic dissection.
We included 320 patients with suspected acute aortic syndrome from three centers (Beijing Anzhen Hospital Affiliated to Capital Medical University, Fujian Provincial Hospital, and Xiangya Hospital of Central South University) between May 2020 and May 2022 in this retrospective study. All patients underwent simultaneous NCE-CT and contrast-enhanced CT (CE-CT). The cohort comprised 160 patients with aortic dissection and 160 without aortic dissection. A deep learning algorithm, three-dimensional (3D) full-resolution U-Net, was continuously trained and refined to segment the true and false lumens of the aorta to determine the presence of aortic dissection. The algorithm's efficacy in detecting dissections was evaluated using the receiver operating characteristic (ROC) curve, including the area under the curve (AUC), sensitivity, and specificity. Furthermore, a comparative analysis of the diagnostic capabilities between our algorithm and three radiologists was conducted.
In diagnosing aortic dissection using NCE-CT images, the developed algorithm demonstrated an accuracy of 93.8% [95% confidence interval (CI): 89.8-98.3%], a sensitivity of 91.6% (95% CI: 86.7-95.8%), and a specificity of 95.6% (95% CI: 91.2-99.3%). In contrast, the radiologists achieved an accuracy of 88.8% (95% CI: 83.5-94.1%), a sensitivity of 90.6% (95% CI: 83.5-94.1%), and a specificity of 94.1% (95% CI: 72.9-97.6%). There was no significant difference between the algorithm's performance and radiologists' mean performance in accuracy, sensitivity, or specificity (P>0.05).
The algorithm proficiently segments the true and false lumens in aortic NCE-CT images, exhibiting diagnostic capabilities comparable to those of radiologists in detecting aortic dissection. This suggests that the algorithm could reduce misdiagnoses in clinical practice, thereby enhancing patient care.
主动脉夹层是一种危及生命的临床急症，但由于诊断技术的局限性，其常被漏诊和误诊。在本研究中，我们开发了一种基于深度学习的算法，用于在非增强计算机断层扫描（NCE-CT）图像上识别主动脉的真腔和假腔，并确定是否存在主动脉夹层。此外，我们还比较了该算法与放射科医生在检测主动脉夹层方面的诊断性能。
在这项回顾性研究中，我们纳入了2020年5月至2022年5月期间来自三个中心（首都医科大学附属北京安贞医院、福建省立医院和中南大学湘雅医院）的320例疑似急性主动脉综合征患者。所有患者均同时接受了NCE-CT和增强CT（CE-CT）检查。该队列包括160例主动脉夹层患者和160例无主动脉夹层患者。一种深度学习算法，即三维（3D）全分辨率U-Net，经过持续训练和优化，以分割主动脉的真腔和假腔，从而确定是否存在主动脉夹层。使用受试者操作特征（ROC）曲线评估该算法在检测夹层方面的效能，包括曲线下面积（AUC）、敏感性和特异性。此外，还对我们的算法与三位放射科医生的诊断能力进行了对比分析。
在使用NCE-CT图像诊断主动脉夹层时，所开发的算法准确率为93.8%[95%置信区间（CI）：89.8-98.3%]，敏感性为91.6%（95%CI：86.7-95.8%），特异性为95.6%（95%CI：91.2-99.3%）。相比之下，放射科医生的准确率为88.8%（95%CI：83.5-94.1%），敏感性为90.6%（95%CI：83.5-94.1%），特异性为94.1%（95%CI：72.9-97.6%）。该算法的性能与放射科医生的平均性能在准确率、敏感性或特异性方面无显著差异（P>0.05）。
该算法能够熟练地分割主动脉NCE-CT图像中的真腔和假腔，在检测主动脉夹层方面表现出与放射科医生相当的诊断能力。这表明该算法可减少临床实践中的误诊，从而改善患者护理。
