Abstract

Marfan syndrome (MFS) is a genetic connective tissue disorder caused by a variant of the fibrillin-1 (FBN1) gene, leading to abnormalities in organs reliant on tissue elasticity. Proximal thoracic aortic aneurysms (TAAs) are a major clinical concern, particularly in pediatric MFS patients, due to their risk of dissection or rupture. Diagnosis and monitoring of TAAs often depend on manual measurements of aortic root diameters from the parasternal long axis views of transthoracic echocardiograms. However, this method is prone to interobserver variability (IOV), which impacts consistency in clinical assessment. To mitigate these limitations, we developed a graphical user interface (GUI) that incorporates a novel feature tracking algorithm for the aortic root boundaries and extracts the diameter values across time at physiologically significant locations from anatomical M-mode images, along with Green-Lagrange Circumferential Strain (GLCS) from standard echocardiographic image data. We evaluated the GUI on a cohort of 28 children between 5 to 10 years of age (14 with MFS and 14 controls). Maximum diameters at the annulus, sinus of valsalva (SoV), sinotubular junction (STJ), and ascending aorta were compared against manual measurements made by a board-certified pediatric cardiologist, with intraclass correlation coefficient (ICC) and linear regression coefficient (R²) values of 0.721/0.5802, 0.977/0.969, 0.528/0.7399, 0.779/0.6302, respectively. As pathophysiologically expected, MFS patients generally showed lower GLCS than controls. This tool enhances the reliability of aortic root assessment by improving precision. Future work will include clinical validation on a larger cohort and integration of deep learning for automated aortic root annotation.

Keywords

Marfan Syndrome, Aortic Aneursym, Echocardiography, Strain

Date of this Version

7-30-2025

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