Osswald Anja, Tsagakis Konstantinos, Thielmann Matthias, Lumsden Alan B, Ruhparwar Arjang, Karmonik Christof
Department of Thoracic and Cardiovascular Surgery, West-German Heart and Vascular Centre, University Duisburg-Essen, 45122 Essen, Germany.
Department of Vascular Surgery, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX 77030, USA.
Diagnostics (Basel). 2024 Dec 18;14(24):2853. doi: 10.3390/diagnostics14242853.
To develop an unsupervised artificial intelligence algorithm for identifying and quantifying the presence of false lumen thrombosis (FL) after Frozen Elephant Trunk (FET) operation in computed tomography angiographic (CTA) images in an interdisciplinary approach.
CTA datasets were retrospectively collected from eight patients after FET operation for aortic dissection from a single center. Of those, five patients had a residual aortic dissection with partial false lumen thrombosis, and three patients had no false lumen or thrombosis. Centerlines of the aortic lumen were defined, and images were calculated perpendicular to the centerline. Lumen and thrombosis were outlined and used as input for a variational autoencoder (VAE) using 2D convolutional neural networks (2D CNN). A 2D latent space was chosen to separate images containing false lumen patency, false lumen thrombosis and no presence of false lumen. Classified images were assigned a thrombus score for the presence or absence of FL thrombosis and an average score for each patient.
Images reconstructed by the trained 2D CNN VAE corresponded well to original images with thrombosis. Average thrombus scores for the five patients ranged from 0.05 to 0.36 where the highest thrombus scores coincided with the location of the largest thrombus lesion. In the three patients without large thrombus lesions, average thrombus scores ranged from 0.002 to 0.01.
The presence and absence of a FL thrombus can be automatically classified by the 2D CNN VAE for patient-specific CTA image datasets. As FL thrombosis is an indication for positive aortic remodeling, evaluation of FL status is essential in follow-up examinations. The presented proof-of-concept is promising for the automated classification and quantification of FL thrombosis.
采用跨学科方法，开发一种无监督人工智能算法，用于在计算机断层血管造影（CTA）图像中识别和量化冷冻象鼻术（FET）术后假腔血栓形成（FL）的存在情况及程度。
回顾性收集来自单一中心的8例接受FET治疗主动脉夹层患者的CTA数据集。其中，5例患者存在残余主动脉夹层并伴有部分假腔血栓形成，3例患者无假腔或血栓形成。定义主动脉腔中心线，并计算垂直于中心线的图像。勾勒出管腔和血栓，并将其用作使用二维卷积神经网络（2D CNN）的变分自编码器（VAE）的输入。选择二维潜在空间来区分包含假腔通畅、假腔血栓形成和无假腔的图像。对分类后的图像根据是否存在FL血栓形成赋予血栓评分，并为每位患者计算平均评分。
经训练的2D CNN VAE重建的图像与有血栓形成的原始图像吻合良好。5例患者的平均血栓评分在0.05至0.36之间，其中最高血栓评分与最大血栓病变的位置一致。在3例无大血栓病变的患者中，平均血栓评分在0.002至0.01之间。
对于特定患者的CTA图像数据集，2D CNN VAE可自动分类FL血栓的有无。由于FL血栓形成是积极主动脉重塑的一个指标，因此在随访检查中评估FL状态至关重要。所展示的概念验证对于FL血栓形成的自动分类和量化具有前景。
