Artificial Intelligence Generated Computed Tomography Segmentation of the Thoracoabdominal Aorta
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Objectives
The rising global burden of cardiovascular diseases (CV) highlights the critical need for efficiency in disease diagnosis and management. An important area for such improvement is utilization of artificial intelligence (AI) for streamlining time and resources in CV imaging workflow. We evaluate the performance of artificial intelligence (AI) segmentation for aortic segmentation on clinical computed tomography angiography (CTA) images and compare accuracy to manual methods. Such automation would markedly improve efficiency and accuracy of aortic surveillance.
Methods
This retrospective study included 27 scans from 20 patients who underwent thoracic endovascular aortic repair (TEVAR) between January 2020 and March 2022. An open-source AI model was applied to segment the aorta, and its performance was assessed by comparing AI-generated segmentations with manual segmentations using Dice similarity coefficients, volumetric analysis, and aortic dimensions. Centerline reconstructed images of thoracoabdominal aorta were processed to extract radiomic features, including maximum diameter and cross-sectional area, for analysis.
Results
The AI tool achieved a median Dice coefficient of 0.96 (0.02), indicating a high degree of concordance with manual segmentation. Multiplanar reconstruction was performed to visualize the aorta and extract measurements along its length using the automated centerline, and radiomic features, including maximum diameter and cross-sectional area, were subsequently extracted for analysis.
Conclusions
AI segmentation demonstrates strong potential for improving efficiency and consistency in thoracoabdominal aortic segmentation, achieving high accuracy compared to manual methods. These findings highlight the feasibility of AI integration into clinical practice for diagnosis and surveillance of aortopathies, warranting further validation on larger datasets to enable clinical translation.
