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Diagnostic performance of deep learning-based vascular extraction and stenosis detection technique for coronary artery disease.

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。

  • 影响因子:2.12
  • DOI:10.1259/bjr.20191028
  • 作者列表:"Chen M","Wang X","Hao G","Cheng X","Ma C","Guo N","Hu S","Tao Q","Yao F","Hu C
  • 发表时间:2020-09-01
Abstract

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) (p < 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.

摘要

目的: 探讨基于深度学习 (DL) 的血管提取和狭窄检测技术在评估冠心病 (CAD) 中的诊断性能。 方法: 以有创冠状动脉造影为参考标准,回顾性分析124例疑似CAD患者的冠状动脉ct血管造影结果,评价DL技术的诊断效能。管腔直径狭窄 ≥ 50% 被认为是阻塞性的,在每例患者、每支血管和每段水平评估诊断性能。通过接受者操作特征曲线 (auc) 下的面积比较DL模型和读取器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 [灵敏度为94%,特异度为63%,阳性预测值为94%,阴性预测值为59%],而reader模型的AUC为0.74 (灵敏度为97%,特异度为50%,阳性预测值为93%,73%) 的阴性预测值。在基于血管的分析中,DL模型和读取器模型的auc分别为0.87和0.89。在基于片段的分析中,分别通过DL模型和读取器模型获得0.84和0.89的auc。DL模型分析每个患者的所有节段花费0.47  min,显著少于reader模型 (29.65  min) (p <0.001)。 结论: DL技术能准确、有效地识别梗阻性CAD,耗时少,是一种可靠的诊断手段。 知识进展: DL技术具有检测CAD的诊断能力,具有宝贵的前景。

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作者列表:["Dev M","Sharma M","Rana N"]

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心脏影像技术方向

心脏结构和心脏血流的可视化,用于诊断评估或通过内窥镜、放射性核素成像等技术来指导心脏手术。

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