Aortic root changes before and after surgery for chronic aortic dilatation: A 3D echocardiographic study

Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes

Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid-solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.

Relationship Between Leaflets and Root in Normal Aortic Valve Based on Computed Tomography Imaging: Implication for Aortic Valve Repair

Objective: By assessing the normal dimensions and the relationship between the aortic root and leaflets in Chinese population, the objective of this three-dimensional computed tomography (3DCT)-based study was to establish a matching reference for leaflets and aortic root for aortic valve (AV) repair.

Method: Electrocardiogram-gated multi-detector CT was performed on 168 Chinese participants with a normal aortic valve. Measurements of the aortic annuli and leaflets were obtained. The correlations between and the ratios of the specific root and leaflet measurements were analyzed. The references for the leaflet and root dimensions were suggested based on geometric height (gH) using a linear regression equation. The utility of the ratios was tested with CT images of 15 patients who underwent aortic valve repair.

Result: The mean annulus diameter (AD), sino-tubular junction (STJ) diameter, geometric height (gH), effective height (eH), free margin length (FML), commissural height (ComH), inter-commissural distance (ICD), and coaptation height (CH) were 22.4 ± 1.7 mm, 27.3 ± 2, 0.4 mm, 15.5 ± 1.7 mm, 8.9 ± 1.2 mm, 32.0 ± 3.4 mm, 17.9 ± 1.9 mm, 23.1 ± 2.3 mm, and 3.1 ± 0.6 mm, respectively. The gH/AD, FML/ICD, and eH/ComH ratios were 0.69 ± 0.07, 1.38 ± 0.08, and 0.50 ± 0.07, respectively. The gH correlated with all other leaflet and root measurements (P < 0.01), whereas the FML demonstrated a better correlation with ICD compared with gH (R² = 0.75, and R² = 0.37, respectively). The FML/ICD and eH/ComH ratios might be used to assess leaflet-root mismatch and post-repair leaflet billowing.

Conclusion: The normal aortic valve measurements based on 3DCT revealed a specific relationship between the root and leaflets; and this will guide the development of an objective method of aortic valve repair.