Fink Nicola, Yacoub Basel, Schoepf U Joseph, Zsarnoczay Emese, Pinos Daniel, Vecsey-Nagy Milan, Rapaka Saikiran, Sharma Puneet, O'Doherty Jim, Ricke Jens, Varga-Szemes Akos, Emrich Tilman
Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA.
Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany.
Diagnostics (Basel). 2024 Apr 23;14(9):866. doi: 10.3390/diagnostics14090866.
This study evaluated a deep neural network (DNN) algorithm for automated aortic diameter quantification and aortic dissection detection in chest computed tomography (CT). A total of 100 patients (median age: 67.0 [interquartile range 55.3/73.0] years; 60.0% male) with aortic aneurysm who underwent non-enhanced and contrast-enhanced electrocardiogram-gated chest CT were evaluated. All the DNN measurements were compared to manual assessment, overall and between the following subgroups: (1) ascending (AA) vs. descending aorta (DA); (2) non-obese vs. obese; (3) without vs. with aortic repair; (4) without vs. with aortic dissection. Furthermore, the presence of aortic dissection was determined (yes/no decision). The automated and manual diameters differed significantly ( < 0.05) but showed excellent correlation and agreement (r = 0.89; ICC = 0.94). The automated and manual values were similar in the AA group but significantly different in the DA group ( < 0.05), similar in obese but significantly different in non-obese patients ( < 0.05) and similar in patients without aortic repair or dissection but significantly different in cases with such pathological conditions ( < 0.05). However, in all the subgroups, the automated diameters showed strong correlation and agreement with the manual values (r > 0.84; ICC > 0.9). The accuracy, sensitivity and specificity of DNN-based aortic dissection detection were 92.1%, 88.1% and 95.7%, respectively. This DNN-based algorithm enabled accurate quantification of the largest aortic diameter and detection of aortic dissection in a heterogenous patient population with various aortic pathologies. This has the potential to enhance radiologists' efficiency in clinical practice.
本研究评估了一种深度神经网络（DNN）算法，用于在胸部计算机断层扫描（CT）中自动定量主动脉直径和检测主动脉夹层。共有100例患有主动脉瘤的患者（中位年龄：67.0[四分位间距55.3/73.0]岁；60.0%为男性）接受了非增强和对比增强心电图门控胸部CT检查，并进行了评估。将所有DNN测量结果与手动评估进行比较，整体比较以及在以下亚组之间进行比较：（1）升主动脉（AA）与降主动脉（DA）；（2）非肥胖与肥胖；（3）未进行主动脉修复与进行主动脉修复；（4）无主动脉夹层与有主动脉夹层。此外，确定主动脉夹层的存在情况（是/否判定）。自动测量的直径与手动测量的直径存在显著差异（<0.05），但显示出极佳的相关性和一致性（r = 0.89；ICC = 0.94）。自动测量值与手动测量值在AA组中相似，但在DA组中存在显著差异（<0.05），在肥胖患者中相似，但在非肥胖患者中存在显著差异（<0.05），在未进行主动脉修复或无主动脉夹层的患者中相似，但在有此类病理情况的病例中存在显著差异（<0.05）。然而，在所有亚组中，自动测量的直径与手动测量值均显示出强相关性和一致性（r>0.84；ICC>0.9）。基于DNN的主动脉夹层检测的准确性、敏感性和特异性分别为92.1%、88.1%和95.7%。这种基于DNN的算法能够在患有各种主动脉病变的异质性患者群体中准确量化最大主动脉直径并检测主动脉夹层。这有可能提高放射科医生在临床实践中的效率。
