Abstract：
The coronary artery disease reporting and data system (CAD-RADS™) was recently introduced to standardise reporting. We aimed to evaluate the utility of an automatic postprocessing and reporting system based on CAD-RADS™ in suspected coronary artery disease (CAD) patients.
Clinical evaluation was performed in 346 patients who underwent coronary computed tomography angiography (CCTA). We compared deep learning (DL)-based CCTA with human readers for evaluation of CAD-RADS™ with commercially-available automated segmentation and manual postprocessing in a retrospective validation cohort.
Compared with invasive coronary angiography, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the DL model for diagnosis of CAD were 79.02%, 86.52%, 89.50%, 73.94%, and 82.08%, respectively. There was no significant difference between the DL-based and the reader-based CAD-RADS™ grading of CCTA results. Consistency testing showed that the Kappa value between the model and the readers was 0.775 (95% confidence interval [CI]: 0.728-0.823, p < 0.001), 0.802 (95% CI: 0.756-0.847, p < 0.001), and 0.796 (95% CI: 0.750-0.843, p < 0.001), respectively. This system reduces the time taken from 14.97 ± 1.80 min to 5.02 ± 0.8 min (p < 0.001).
The standardised reporting of DL-based CAD-RADS™ in CCTA can accurately and rapidly evaluate suspected CAD patients, and has good consistency with grading by radiologists.
Clinical evaluation was performed in 346 patients who underwent coronary computed tomography angiography (CCTA). We compared deep learning (DL)-based CCTA with human readers for evaluation of CAD-RADS™ with commercially-available automated segmentation and manual postprocessing in a retrospective validation cohort.
Compared with invasive coronary angiography, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the DL model for diagnosis of CAD were 79.02%, 86.52%, 89.50%, 73.94%, and 82.08%, respectively. There was no significant difference between the DL-based and the reader-based CAD-RADS™ grading of CCTA results. Consistency testing showed that the Kappa value between the model and the readers was 0.775 (95% confidence interval [CI]: 0.728-0.823, p < 0.001), 0.802 (95% CI: 0.756-0.847, p < 0.001), and 0.796 (95% CI: 0.750-0.843, p < 0.001), respectively. This system reduces the time taken from 14.97 ± 1.80 min to 5.02 ± 0.8 min (p < 0.001).
The standardised reporting of DL-based CAD-RADS™ in CCTA can accurately and rapidly evaluate suspected CAD patients, and has good consistency with grading by radiologists.
摘要：
目的：冠状动脉疾病报告和数据系统（CAD-RADS™）最近被引入标准化报告。我们旨在评估基于CAD-RADS™的自动后处理和报告系统在可疑冠状动脉疾病（CAD）患者中的实用性。
方法：对346例接受冠状动脉CT血管造影（CCTA）的患者进行临床评估。我们将基于深度学习（DL）的CCTA与人类读者进行了比较，以评估CAD-RADS™，并在回顾性验证队列中使用市售的自动分割和手动后处理。
结果：与有创冠状动脉造影相比，灵敏度,特异性，正预测值，负预测值，DL模型诊断CAD的准确率为79.02%，86.52%,89.50%,73.94%,和82.08%，分别。CCTA结果的基于DL和基于阅读器的CAD-RADS™分级之间没有显著差异。一致性测试表明，模型与读者之间的Kappa值为0.775（95％置信区间[CI]：0.728-0.823，p<0.001），0.802（95%CI：0.756-0.847，p<0.001），和0.796（95%CI：0.750-0.843，p<0.001），分别。该系统将所需的时间从14.97±1.80分钟减少到5.02±0.8分钟(p<0.001)。
结论：CCTA中基于DL的CAD-RADS™的标准化报告可以准确快速地评估疑似CAD患者，与放射科医师的分级具有良好的一致性。
方法：对346例接受冠状动脉CT血管造影（CCTA）的患者进行临床评估。我们将基于深度学习（DL）的CCTA与人类读者进行了比较，以评估CAD-RADS™，并在回顾性验证队列中使用市售的自动分割和手动后处理。
结果：与有创冠状动脉造影相比，灵敏度,特异性，正预测值，负预测值，DL模型诊断CAD的准确率为79.02%，86.52%,89.50%,73.94%,和82.08%，分别。CCTA结果的基于DL和基于阅读器的CAD-RADS™分级之间没有显著差异。一致性测试表明，模型与读者之间的Kappa值为0.775（95％置信区间[CI]：0.728-0.823，p<0.001），0.802（95%CI：0.756-0.847，p<0.001），和0.796（95%CI：0.750-0.843，p<0.001），分别。该系统将所需的时间从14.97±1.80分钟减少到5.02±0.8分钟(p<0.001)。
结论：CCTA中基于DL的CAD-RADS™的标准化报告可以准确快速地评估疑似CAD患者，与放射科医师的分级具有良好的一致性。
