Pradella Maurice, Achermann Rita, Sperl Jonathan I, Kärgel Rainer, Rapaka Saikiran, Cyriac Joshy, Yang Shan, Sommer Gregor, Stieltjes Bram, Bremerich Jens, Brantner Philipp, Sauter Alexander W
Department of Radiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland.
Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
Front Cardiovasc Med. 2022 Aug 22;9:972512. doi: 10.3389/fcvm.2022.972512. eCollection 2022.
Thoracic aortic (TA) dilatation (TAD) is a risk factor for acute aortic syndrome and must therefore be reported in every CT report. However, the complex anatomy of the thoracic aorta impedes TAD detection. We investigated the performance of a deep learning (DL) prototype as a secondary reading tool built to measure TA diameters in a large-scale cohort.
Consecutive contrast-enhanced (CE) and non-CE chest CT exams with "normal" TA diameters according to their radiology reports were included. The DL-prototype (AIRad, Siemens Healthineers, Germany) measured the TA at nine locations according to AHA guidelines. Dilatation was defined as >45 mm at aortic sinus, sinotubular junction (STJ), ascending aorta (AA) and proximal arch and >40 mm from mid arch to abdominal aorta. A cardiovascular radiologist reviewed all cases with TAD according to AIRad. Multivariable logistic regression (MLR) was used to identify factors (demographics and scan parameters) associated with TAD classification by AIRad.
18,243 CT scans (45.7% female) were successfully analyzed by AIRad. Mean age was 62.3 ± 15.9 years and 12,092 (66.3%) were CE scans. AIRad confirmed normal diameters in 17,239 exams (94.5%) and reported TAD in 1,004/18,243 exams (5.5%). Review confirmed TAD classification in 452/1,004 exams (45.0%, 2.5% total), 552 cases were false-positive but identification was easily possible using visual outputs by AIRad. MLR revealed that the following factors were significantly associated with correct TAD classification by AIRad: TAD reported at AA [odds ratio (OR): 1.12, < 0.001] and STJ (OR: 1.09, = 0.002), TAD found at >1 location (OR: 1.42, = 0.008), in CE exams (OR: 2.1-3.1, < 0.05), men (OR: 2.4, = 0.003) and patients presenting with higher BMI (OR: 1.05, = 0.01). Overall, 17,691/18,243 (97.0%) exams were correctly classified.
AIRad correctly assessed the presence or absence of TAD in 17,691 exams (97%), including 452 cases with previously missed TAD independent from contrast protocol. These findings suggest its usefulness as a secondary reading tool by improving report quality and efficiency.
胸主动脉（TA）扩张（TAD）是急性主动脉综合征的危险因素，因此必须在每份CT报告中予以报告。然而，胸主动脉复杂的解剖结构阻碍了TAD的检测。我们研究了一种深度学习（DL）原型作为辅助阅读工具在大规模队列中测量TA直径的性能。
纳入根据放射学报告TA直径“正常”的连续增强对比（CE）和非CE胸部CT检查。DL原型（AIRad，德国西门子医疗）根据美国心脏协会（AHA）指南在九个位置测量TA。扩张定义为主动脉窦、窦管交界（STJ）、升主动脉（AA）和近端弓处直径>45mm，从中弓到腹主动脉直径>40mm。一位心血管放射科医生根据AIRad对所有TAD病例进行复查。多变量逻辑回归（MLR）用于确定与AIRad对TAD分类相关的因素（人口统计学和扫描参数）。
AIRad成功分析了18243例CT扫描（45.7%为女性）。平均年龄为62.3±15.9岁，12092例（66.3%）为CE扫描。AIRad在17239例检查（94.5%）中确认直径正常，在18243例检查中的1004例（5.5%）中报告有TAD。复查在1004例检查中的452例（45.0%，占总数的2.5%）中确认了TAD分类，552例为假阳性，但通过AIRad的视觉输出很容易识别。MLR显示，以下因素与AIRad对TAD的正确分类显著相关：AA处报告有TAD[比值比（OR）：1.12，<0.001]和STJ处（OR：1.09，=0.002），在>1个位置发现TAD（OR：1.42，=0.008），在CE检查中（OR：2.1 - 3.1，<0.05），男性（OR：2.4，=0.003）以及BMI较高的患者（OR：1.05，=0.01）。总体而言，18243例检查中的17691例（97.0%）分类正确。
AIRad在17691例检查（97%）中正确评估了TAD的有无，包括452例之前漏诊的TAD病例，且与对比方案无关。这些发现表明它作为辅助阅读工具可提高报告质量和效率，具有实用性。
