# 3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata

## TL;DR

This paper introduces a novel deep neural network that combines CMR images and patient metadata to accurately predict 3D cardiac shapes, facilitating large-scale automatic analysis in epidemiological studies.

## Contribution

It is the first approach to integrate images and metadata for 3D cardiac shape prediction using deep learning, leveraging statistical shape models and CNNs.

## Key findings

- Broadly significant agreement with reference shapes
- Accurate estimation of ventricular volume and myocardial mass
- Effective 3D shape prediction validated on 500 cases

## Abstract

Large prospective epidemiological studies acquire cardiovascular magnetic resonance (CMR) images for pre-symptomatic populations and follow these over time. To support this approach, fully automatic large-scale 3D analysis is essential. In this work, we propose a novel deep neural network using both CMR images and patient metadata to directly predict cardiac shape parameters. The proposed method uses the promising ability of statistical shape models to simplify shape complexity and variability together with the advantages of convolutional neural networks for the extraction of solid visual features. To the best of our knowledge, this is the first work that uses such an approach for 3D cardiac shape prediction. We validated our proposed CMR analytics method against a reference cohort containing 500 3D shapes of the cardiac ventricles. Our results show broadly significant agreement with the reference shapes in terms of the estimated volume of the cardiac ventricles, myocardial mass, 3D Dice, and mean and Hausdorff distance.