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Ju-P2: Sesión de pósteres II Lugar: Zona pósteres | |
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Deep Learning-Based Prediction of Wall Shear Stress from Thoracic Aorta Geometry CoMMLab, Departament d'Informàtica. Universitat de València, España Wall Shear Stress (WSS) is a key biomechanical factor in the progression of aortic diseases such as aneurysms and dissections. While Computational Fluid Dynamics (CFD) is the standard for WSS estimation, its high computational cost and complexity limit its routine use in clinical practice. The objective of this work is to develop and validate a deep learning (DL) framework capable of predicting high-resolution WSS maps directly from aortic geometry, providing a fast and accurate surrogate for CFD. Predicted WSS maps exhibit strong visual agreement with CFD references, accurately reproducing both the location and magnitude of key hemodynamic features. Good linear correlation is observed between predicted and ground-truth values, with a slight tendency to underpredict extreme WSS peaks. | |