Myocardial infarction (MI) is one of the most common causes of death in the world. Image-based biomarkers commonly used in the clinic, such as ejection fraction, fail to capture more complex patterns in the heart's 3D anatomy and thus limit diagnostic accuracy. In this work, we present the multi-objective point cloud autoencoder as a novel geometric deep learning approach for explainable infarction prediction, based on multi-class 3D point cloud representations of cardiac anatomy and function. Its architecture consists of multiple task-specific branches connected by a low-dimensional latent space to allow for effective multi-objective learning of both reconstruction and MI prediction, while capturing pathology-specific 3D shape information in an interpretable latent space. Furthermore, its hierarchical branch design with point cloud-based deep learning operations enables efficient multi-scale feature learning directly on high-resolution anatomy point clouds. In our experiments on a large UK Biobank dataset, the multi-objective point cloud autoencoder is able to accurately reconstruct multi-temporal 3D shapes with Chamfer distances between predicted and input anatomies below the underlying images' pixel resolution. Our method outperforms multiple machine learning and deep learning benchmarks for the task of incident MI prediction by 19% in terms of Area Under the Receiver Operating Characteristic curve. In addition, its task-specific compact latent space exhibits easily separable control and MI clusters with clinically plausible associations between subject encodings and corresponding 3D shapes, thus demonstrating the explainability of the prediction.

翻译：心肌梗死（MI）是全球最普遍的致死原因之一。临床常规使用的影像生物标志物（如射血分数）难以捕捉心脏三维解剖结构的复杂模式，从而限制了诊断准确性。本文提出多目标点云自编码器作为新型几何深度学习方法，基于心脏解剖与功能的多类三维点云表征，实现可解释的梗死预测。其架构由多个任务专用分支通过低维潜在空间连接而成，在捕捉病理特异性三维形态信息的同时，通过可解释潜在空间实现重建与MI预测的高效多目标学习。此外，基于点云深度学习操作的分层分支设计，可直接在高分辨率解剖点云上进行高效多尺度特征学习。在英国生物银行大样本数据集实验表明，该多目标点云自编码器能精确重建多时相三维形状，预测与输入解剖结构之间的Chamfer距离低于原始影像像素分辨率。在突发性MI预测任务中，本方法相较多种机器学习和深度学习基准模型，受试者工作特征曲线下面积（AUC）提升19%。其任务专用紧凑潜在空间展现出易于区分的对照组与MI簇，且受试者编码与对应三维形状之间存在临床合理的关联性，从而验证了预测的可解释性。