# 3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks

arXiv: https://arxiv.org/abs/2307.07298 · HTML: https://mlsanity.com/paper/2307.07298/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 14 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2201.03186/) (2022)
- [Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images](https://mlsanity.com/paper/1708.01141/) (2017)
- [Deep Computational Model for the Inference of Ventricular Activation Properties](https://mlsanity.com/paper/2208.04028/) (2022)
- [One-stage Shape Instantiation from a Single 2D Image to 3D Point Cloud](https://mlsanity.com/paper/1907.10763/) (2019)
- [PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data](https://mlsanity.com/paper/2008.08194/) (2020)

## Built on by (most cited first, 4 of 4)

- [Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images](https://mlsanity.com/paper/2307.08535/) (2023)
- [Modeling 3D cardiac contraction and relaxation with point cloud deformation networks](https://mlsanity.com/paper/2307.10927/) (2023)
- [Multi-objective point cloud autoencoders for explainable myocardial infarction prediction](https://mlsanity.com/paper/2307.11017/) (2023)
- [3D Cardiac Anatomy Generation Using Mesh Latent Diffusion Models](https://mlsanity.com/paper/2508.14122/) (2025)

## Abstract (arXiv)

Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases with associated clinical decision-making typically based on single-valued imaging biomarkers. However, such metrics only approximate the complex 3D structure and physiology of the heart and hence hinder a better understanding and prediction of MI outcomes. In this work, we investigate the utility of complete 3D cardiac shapes in the form of point clouds for an improved detection of MI events. To this end, we propose a fully automatic multi-step pipeline consisting of a 3D cardiac surface reconstruction step followed by a point cloud classification network. Our method utilizes recent advances in geometric deep learning on point clouds to enable direct and efficient multi-scale learning on high-resolution surface models of the cardiac anatomy. We evaluate our approach on 1068 UK Biobank subjects for the tasks of prevalent MI detection and incident MI prediction and find improvements of ~13% and ~5% respectively over clinical benchmarks. Furthermore, we analyze the role of each ventricle and cardiac phase for 3D shape-based MI detection and conduct a visual analysis of the morphological and physiological patterns typically associated with MI outcomes.