基于深度学习的血管提取和狭窄检测技术对冠心病的诊断性能。
Diagnostic performance of deep learning-based vascular extraction and stenosis detection technique for coronary artery disease.
机构信息
Department of Radiology, The First Affiliated Hospital of Soochow University, NO.899 Pinghai Road, Gusu District, Suzhou, Jiangsu, China.
Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu, China.
出版信息
Br J Radiol. 2020 Sep 1;93(1113):20191028. doi: 10.1259/bjr.20191028. Epub 2020 Mar 25.
OBJECTIVE
To investigate the diagnostic performance of deep learning (DL)-based vascular extraction and stenosis detection technology in assessing coronary artery disease (CAD).
METHODS
The diagnostic performance of DL technology was evaluated by retrospective analysis of coronary computed tomography angiography in 124 suspected CAD patients, using invasive coronary angiography as reference standard. Lumen diameter stenosis ≥50% was considered obstructive, and the diagnostic performances were evaluated at per-patient, per-vessel and per-segment levels. The diagnostic performances between DL model and reader model were compared by the areas under the receiver operating characteristics curves (AUCs).
RESULTS
In patient-based analysis, AUC of 0.78 was obtained by DL model to detect obstructive CAD [sensitivity of 94%, specificity of 63%, positive predictive value of 94%, and negative predictive value of 59%], While AUC by reader model was 0.74 (sensitivity of 97%, specificity of 50%, positive predictive value of 93%, negative predictive value of 73%). In vessel-based analysis, the AUCs of DL model and reader model were 0.87 and 0.89 respectively. In segment-based analysis, the AUCs of 0.84 and 0.89 were obtained by DL model and reader model respectively. It took 0.47 min to analyze all segments per patient by DL model, which is significantly less than reader model (29.65 min) ( < 0.001).
CONCLUSION
The DL technology can accurately and effectively identify obstructive CAD, with less time-consuming, and it could be a reliable diagnostic tool to detect CAD.
ADVANCES IN KNOWLEDGE
The DL technology has valuable prospect with the diagnostic ability to detect CAD.
目的
研究基于深度学习（DL）的血管提取和狭窄检测技术在评估冠状动脉疾病（CAD）中的诊断性能。
方法
通过回顾性分析 124 例疑似 CAD 患者的冠状动脉计算机断层血管造影，以有创冠状动脉造影为参考标准，评估 DL 技术的诊断性能。将管腔直径狭窄≥50%定义为阻塞性，在患者、血管和节段水平上评估诊断性能。通过受试者工作特征曲线下面积（AUC）比较 DL 模型和读者模型之间的诊断性能。
结果
在基于患者的分析中，DL 模型检测阻塞性 CAD 的 AUC 为 0.78[敏感度为 94%，特异度为 63%，阳性预测值为 94%，阴性预测值为 59%]，而读者模型的 AUC 为 0.74[敏感度为 97%，特异度为 50%，阳性预测值为 93%，阴性预测值为 73%]。在基于血管的分析中，DL 模型和读者模型的 AUC 分别为 0.87 和 0.89。在基于节段的分析中，DL 模型和读者模型的 AUC 分别为 0.84 和 0.89。DL 模型分析每位患者所有节段的时间为 0.47 分钟，明显少于读者模型（29.65 分钟）（<0.001）。
结论
DL 技术可以准确有效地识别阻塞性 CAD，且耗时更少，可能是一种可靠的 CAD 诊断工具。
知识进展
DL 技术具有诊断 CAD 的有价值前景。
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