用于临床疾病评估的基于物理条件神经算子的实时可泛化心脏瓣膜力学
A Physics-Conditioned Neural Operator for Generalization of Atrioventricular Valve Mechanics across Pressure and Tissue Properties
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中文总结 AI 辅助
本文提出物理条件神经算子（PCNO），用于快速预测心脏瓣膜力学，较有限元模拟加速上万倍，并准确识别病理类别，助力临床早期干预。
中文摘要 AI 辅助
二尖瓣反流是全球最常见的心脏瓣膜疾病，影响全球超过2%的人口，在75岁以上成年人中患病率至少达到10%，并导致约15%的瓣膜性心脏病相关死亡。然而，只有少数重症患者接受了矫正手术。快速评估瓣膜力学性能可以促进更早、更精确的干预，但传统的有限元模拟对于临床时间线和参数扫描而言仍然过于缓慢。我们引入了物理条件神经算子（PCNO），这是一种基于Transformer的替代模型，能够预测二尖瓣和三尖瓣几何结构上的瓣叶位移、应变和应力场，并以收缩压和组织特性为条件。该模型在功能性、反流性和病理性瓣膜（包括腱索牵拉、P2脱垂和瓣环扩张）上训练，相较于细网格有限元模拟，PCNO实现了高达15,260倍的加速，同时保持相当的精度，能够识别病理类别，并在分布外外推情况下以3.5%以内的误差解析诊断指标。
英文摘要
Mitral and tricuspid regurgitation are the most common regurgitant valvular lesions, yet only a minority of severe cases undergo corrective surgery. Rapid assessment of valve mechanics could enable earlier, more precise intervention, but finite element (FE) analysis is slow to repeat across the many loading and tissue-property values of interest, which for a given valve are not known in advance. We introduce the Physics-Conditioned Neural Operator (PCNO), a transformer-based neural operator predicting leaflet displacement, strain, and stress fields conditioned on systolic blood pressure and tissue properties. PCNO is trained on, and evaluated against, FEBio simulations of functional and regurgitant mitral and tricuspid valves and of three mitral pathologies. With pressure and all material parameters simultaneously outside the training support, displacement error against FE reaches 4.48% and the mean errors of unsupervised geometric measures of valve function stay within 3.5%, indicating a conditioned solution operator over parameter space rather than an interpolator of the training set. Under this shift, PCNO is also more accurate than graph neural network and graph neural operator baselines trained on the same simulations, with the largest margins in stress.
发表机构
- University of Pennsylvania（宾夕法尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。
