Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Siemens Healthineers, Erlangen, Germany.
Eur J Cardiothorac Surg. 2024 Jun 3;65(6). doi: 10.1093/ejcts/ezae219.
To assess the accuracy of a deep learning-based algorithm for fully automated detection of thoracic aortic calcifications in chest computed tomography (CT) with a focus on the aortic clamping zone.
We retrospectively included 100 chest CT scans from 91 patients who were examined on second- or third-generation dual-source scanners. Subsamples comprised 47 scans with an electrocardiogram-gated aortic angiography and 53 unenhanced scans. A deep learning model performed aortic landmark detection and aorta segmentation to derive 8 vessel segments. Associated calcifications were detected and their volumes measured using a mean-based density thresholding. Algorithm parameters (calcium cluster size threshold, aortic mask dilatation) were varied to determine optimal performance for the upper ascending aorta that encompasses the aortic clamping zone. A binary visual rating served as a reference. Standard estimates of diagnostic accuracy and inter-rater agreement using Cohen's Kappa were calculated.
Thoracic aortic calcifications were observed in 74% of patients with a prevalence of 27-70% by aorta segment. Using different parameter combinations, the algorithm provided binary ratings for all scans and segments. The best performing parameter combination for the presence of calcifications in the aortic clamping zone yielded a sensitivity of 93% and a specificity of 82%, with an area under the receiver operating characteristic curve of 0.874. Using these parameters, the inter-rater agreement ranged from κ 0.66 to 0.92 per segment.
Fully automated segmental detection of thoracic aortic calcifications in chest CT performs with high accuracy. This includes the critical preoperative assessment of the aortic clamping zone.
评估一种基于深度学习的算法在胸部 CT 中全自动检测胸主动脉钙化的准确性，重点关注主动脉夹闭区。
我们回顾性纳入了 91 例患者的 100 例胸部 CT 扫描，这些患者在第二代或第三代双源扫描仪上进行了检查。亚组包括 47 例心电图门控主动脉血管造影扫描和 53 例未增强扫描。深度学习模型进行主动脉标志检测和主动脉分割，以获得 8 个血管段。使用基于均值的密度阈值检测相关钙化并测量其体积。改变算法参数（钙簇大小阈值、主动脉掩模扩张）以确定涵盖主动脉夹闭区的升主动脉上段的最佳性能。二进制视觉评分作为参考。使用 Cohen's Kappa 计算诊断准确性和组内一致性的标准估计值。
74%的患者观察到胸主动脉钙化，按主动脉段计算的患病率为 27%-70%。使用不同的参数组合，该算法为所有扫描和段提供了二进制评分。在主动脉夹闭区存在钙化的最佳参数组合下，灵敏度为 93%，特异性为 82%，受试者工作特征曲线下面积为 0.874。使用这些参数，每个段的组内一致性范围为κ0.66 至 0.92。
胸部 CT 中胸主动脉钙化的全自动分段检测具有很高的准确性。这包括对主动脉夹闭区的关键术前评估。
