Artzner Christoph, Bongers Malte N, Kärgel Rainer, Faby Sebastian, Hefferman Gerald, Herrmann Judith, Nopper Svenja L, Perl Regine M, Walter Sven S
Department for Diagnostic and Interventional Radiology, University Hospital Tuebingen, Eberhard Karls University Tuebingen, 72076 Tubingen, Germany.
Siemens Healthineers, 91301 Forchheim, Germany.
Diagnostics (Basel). 2022 Jul 23;12(8):1790. doi: 10.3390/diagnostics12081790.
The aim was to evaluate the accuracy of a prototypical artificial intelligence-based algorithm for automated segmentation and diameter measurement of the thoracic aorta (TA) using CT. One hundred twenty-two patients who underwent dual-source CT were retrospectively included. Ninety-three of these patients had been administered intravenous iodinated contrast. Images were evaluated using the prototypical algorithm, which segments the TA and determines the corresponding diameters at predefined anatomical locations based on the American Heart Association guidelines. The reference standard was established by two radiologists individually in a blinded, randomized fashion. Equivalency was tested and inter-reader agreement was assessed using intra-class correlation (ICC). In total, 99.2% of the parameters measured by the prototype were assessable. In nine patients, the prototype failed to determine one diameter along the vessel. Measurements along the TA did not differ between the algorithm and readers (p > 0.05), establishing equivalence. Inter-reader agreement between the algorithm and readers (ICC ≥ 0.961; 95% CI: 0.940−0.974), and between the readers was excellent (ICC ≥ 0.879; 95% CI: 0.818−0.92). The evaluated prototypical AI-based algorithm accurately measured TA diameters at each region of interest independent of the use of either contrast utilization or pathology. This indicates that the prototypical algorithm has substantial potential as a valuable tool in the rapid clinical evaluation of aortic pathology.
目的是评估一种基于人工智能的原型算法在使用CT对胸主动脉（TA）进行自动分割和直径测量时的准确性。回顾性纳入了122例接受双源CT检查的患者。其中93例患者接受了静脉注射碘化造影剂。使用该原型算法对图像进行评估，该算法根据美国心脏协会指南分割TA并在预定义的解剖位置确定相应的直径。由两名放射科医生分别以盲法、随机方式建立参考标准。使用组内相关系数（ICC）测试等效性并评估阅片者间的一致性。总体而言，原型测量的参数中有99.2%是可评估的。在9例患者中，原型未能确定血管沿线的一个直径。算法与阅片者之间沿TA的测量结果无差异（p>0.05），确定了等效性。算法与阅片者之间（ICC≥0.961；95%CI：0.940−0.974）以及阅片者之间的阅片者间一致性极佳（ICC≥0.879；95%CI：0.818−0.92）。所评估的基于人工智能的原型算法能够准确测量每个感兴趣区域的TA直径，与是否使用造影剂或病变无关。这表明该原型算法作为主动脉病变快速临床评估中的一种有价值工具具有巨大潜力。
