用生物力学约束神经网络，让心脏病诊断既准又可解释。
From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification
- 用非线性弹性模型约束图像配准，保证心脏形变物理合理。
- 在ACDC数据集上，心室和心肌分割Dice达0.945、0.908、0.905。
- 分类准确率训练集98%、测试集100%，结果可解释性强。
心血管疾病是全球致病和致死的主要原因，亟需精准及时的诊断手段。本文提出一种融合深度学习图像配准与物理信息正则化的创新方法，用于预测动态心肌组织的生物力学特性并提取疾病分类特征。采用Neo-Hookean材料的能量应变公式建模心肌变形，在优化形变场的同时保证其物理与生物力学一致性。该可解释方法不仅提升了图像配准精度，还揭示了心肌运动背后的力学机制。在自动化心脏诊断挑战赛（ACDC）数据集上的评估显示，左心室腔、右心室腔和心肌的分割Dice分数分别为0.945、0.908和0.905。随后，估算心肌局部应变并提取一组详细特征用于心血管疾病分类。比较五种分类算法（逻辑回归、多层感知机、支持向量机、随机森林、最近邻），通过特征选择确定关键变量。最佳分类器在训练集达到98%准确率，测试集达100%。该方法结合可解释人工智能，使临床医生能基于心脏力学透明理解模型预测，显著提升诊断准确性与可靠性，推动个性化医疗发展。
原文摘要 · Abstract (English)
Cardiac diseases are among the leading causes of morbidity and mortality worldwide, which requires accurate and timely diagnostic strategies. In this study, we introduce an innovative approach that combines deep learning image registration with physics-informed regularization to predict the biomechanical properties of moving cardiac tissues and extract features for disease classification. We utilize the energy strain formulation of Neo-Hookean material to model cardiac tissue deformations, optimizing the deformation field while ensuring its physical and biomechanical coherence. This explainable approach not only improves image registration accuracy, but also provides insights into the underlying biomechanical processes of the cardiac tissues. Evaluation on the Automated Cardiac Diagnosis Challenge (ACDC) dataset achieved Dice scores of 0.945 for the left ventricular cavity, 0.908 for the right ventricular cavity, and 0.905 for the myocardium. Subsequently, we estimate the local strains within the moving heart and extract a detailed set of features used for cardiovascular disease classification. We evaluated five classification algorithms, Logistic Regression, Multi-Layer Perceptron, Support Vector Classifier, Random Forest, and Nearest Neighbour, and identified the most relevant features using a feature selection algorithm. The best performing classifier obtained a classification accuracy of 98% in the training set and 100% in the test set of the ACDC dataset. By integrating explainable artificial intelligence, this method empowers clinicians with a transparent understanding of the model's predictions based on cardiac mechanics, while also significantly improving the accuracy and reliability of cardiac disease diagnosis, paving the way for more personalized and effective patient care.
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