# Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning

arXiv: https://arxiv.org/abs/1708.09843 · HTML: https://mlsanity.com/paper/1708.09843/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2017.

## Built on by (most cited first, 30 of 63)

- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
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- [VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers](https://mlsanity.com/paper/2104.06757/) (2021)
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- [Explanatory Paradigms in Neural Networks](https://mlsanity.com/paper/2202.11838/) (2022)
- [Deep learning on fundus images detects glaucoma beyond the optic disc](https://mlsanity.com/paper/2103.11895/) (2021)
- [Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare](https://mlsanity.com/paper/2302.09813/) (2023)
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- [Disentangling representations of retinal images with generative models](https://mlsanity.com/paper/2402.19186/) (2024)
- [Algorithmic encoding of protected characteristics in image-based models for disease detection](https://mlsanity.com/paper/2110.14755/) (2021)
- [LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models](https://mlsanity.com/paper/2410.01620/) (2024)
- [DR-VNet: Retinal Vessel Segmentation via Dense Residual UNet](https://mlsanity.com/paper/2111.04739/) (2021)
- [On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy](https://mlsanity.com/paper/2106.13497/) (2021)
- [Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI](https://mlsanity.com/paper/2101.06963/) (2021)
- [Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!](https://mlsanity.com/paper/2210.03103/) (2022)
- [k-SALSA: k-anonymous synthetic averaging of retinal images via local style alignment](https://mlsanity.com/paper/2303.10824/) (2023)

## Abstract (arXiv)

Traditionally, medical discoveries are made by observing associations and then designing experiments to test these hypotheses. However, observing and quantifying associations in images can be difficult because of the wide variety of features, patterns, colors, values, shapes in real data. In this paper, we use deep learning, a machine learning technique that learns its own features, to discover new knowledge from retinal fundus images. Using models trained on data from 284,335 patients, and validated on two independent datasets of 12,026 and 999 patients, we predict cardiovascular risk factors not previously thought to be present or quantifiable in retinal images, such as such as age (within 3.26 years), gender (0.97 AUC), smoking status (0.71 AUC), HbA1c (within 1.39%), systolic blood pressure (within 11.23mmHg) as well as major adverse cardiac events (0.70 AUC). We further show that our models used distinct aspects of the anatomy to generate each prediction, such as the optic disc or blood vessels, opening avenues of further research.