# Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders

arXiv: https://arxiv.org/abs/1908.04538 · HTML: https://mlsanity.com/paper/1908.04538/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Review](https://mlsanity.com/paper/2112.12596/) (2021)
- [Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views](https://mlsanity.com/paper/2308.08038/) (2023)
- [DeepSPV: A Deep Learning Pipeline for 3D Spleen Volume Estimation from 2D Ultrasound Images](https://mlsanity.com/paper/2411.11190/) (2024)

## Abstract (arXiv)

Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health. The aim of this study is to analyze the impact of systolic blood pressure (SBP) on cardiac function while preserving the interpretability of the model using known clinical biomarkers in a large cohort of the UK Biobank population. We propose a novel framework that combines deep learning based estimation of interpretable clinical biomarkers from cardiac cine MR data with a variational autoencoder (VAE). The VAE architecture integrates a regression loss in the latent space, which enables the progression of cardiac health with SBP to be learnt. Results on 3,600 subjects from the UK Biobank show that the proposed model allows us to gain important insight into the deterioration of cardiac function with increasing SBP, identify key interpretable factors involved in this process, and lastly exploit the model to understand patterns of positive and adverse adaptation of cardiac function.