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Link to original content: https://doi.org/10.1038/s42256-021-00427-7
Predicting myocardial infarction through retinal scans and minimal personal information | Nature Machine Intelligence
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Predicting myocardial infarction through retinal scans and minimal personal information

Nature Machine Intelligence volume 4, pages 55–61 (2022)Cite this article

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Abstract

In ophthalmologic practice, retinal images are routinely obtained to diagnose and monitor primary eye diseases and systemic conditions affecting the eye, such as diabetic retinopathy. Recent studies have shown that biomarkers on retinal images, for example, retinal blood vessel density or tortuosity, are associated with cardiac function and may identify patients at risk of coronary artery disease. In this work we investigate the use of retinal images, alongside relevant patient metadata, to estimate left ventricular mass and left ventricular end-diastolic volume, and subsequently, predict incident myocardial infarction. We trained a multichannel variational autoencoder and a deep regressor model to estimate left ventricular mass (4.4 (–32.30, 41.1) g) and left ventricular end-diastolic volume (3.02 (–53.45, 59.49) ml) and predict risk of myocardial infarction (AUC = 0.80 ± 0.02, sensitivity = 0.74 ± 0.02, specificity = 0.71 ± 0.03) using just the retinal images and demographic data. Our results indicate that one could identify patients at high risk of future myocardial infarction from retinal imaging available in every optician and eye clinic.

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Fig. 1: STROBE flow diagram for excluded participants.
Fig. 2: Overview of the proposed method.
Fig. 3: Estimation of LVM and LVEDV using manual and automatic annotations.
Fig. 4: Cross-validation results for MI prediction.
Fig. 5: ROC curves obtained from the external validation using AREDS dataset.

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Data availability

All UKB images and demographic data are available, with restrictions, from UKB. Researchers who use the UKB dataset must first complete the UKB online access management system application form. More information on accessing the UKB dataset can be found in this link: https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access. The AREDS dataset (NCT00000145) is available in the dbGAP repository: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000001.v3.p1.

Code availability

All algorithms used in this study were developed using libraries and scripts in PyTorch. The source code is publicly available at https://doi.org/10.5281/zenodo.5716142.

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Acknowledgements

A.F.F. is supported by the Royal Academy of Engineering Chair in Emerging Technologies Scheme (grant no. CiET1819\19), the MedIAN Network (grant no. EP/N026993/1) funded by the Engineering and Physical Sciences Research Council (EPSRC). This work was also supported by the Intramural Research Program of National Library of Medicine and National Eye Institute, National Institutes of Health. This research was also supported by the European Union’s Horizon 2020 InSilc (grant no. 777119) and EPSRC TUSCA (grant no. EP/V04799X/1) programmes. E.D. acknowledges funding from the BHF grant FS/13/71/30378.

Author information

Authors and Affiliations

  1. Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing, University of Leeds, Leeds, UK

    Andres Diaz-Pinto, Nishant Ravikumar, Rahman Attar, Avan Suinesiaputra & Alejandro F. Frangi

  2. Leeds Institute for Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds, UK

    Andres Diaz-Pinto, Nishant Ravikumar, Rahman Attar, Avan Suinesiaputra, Eylem Levelt, Erica Dall’Armellina, Chris P. Gale, Sven Plein & Alejandro F. Frangi

  3. Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials, Technology and Engineering, Chinese Academy of Sciences, Ningbo, China

    Yitian Zhao

  4. Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK

    Eylem Levelt, Erica Dall’Armellina, Chris P. Gale & Sven Plein

  5. Université Côte d’Azur, Inria Sophia Antipolis, Epione Project-Team, Biot, France

    Marco Lorenzi

  6. National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD, USA

    Qingyu Chen & Zhiyong Lu

  7. Division of Epidemiology and Clinical Applications, National Eye Institute, NIH, Bethesda, MD, USA

    Tiarnan D. L. Keenan, Elvira Agrón & Emily Y. Chew

  8. Leeds Institute for Data Analytics, University of Leeds, Leeds, UK

    Chris P. Gale

  9. Department of Ophthalmology, York Teaching Hospital NHS Foundation Trust, York, UK

    Richard P. Gale

  10. Department of Health Sciences, University of York, York, UK

    Richard P. Gale

  11. Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium

    Alejandro F. Frangi

  12. Department of Electrical Engineering, KU Leuven, Leuven, Belgium

    Alejandro F. Frangi

  13. Alan Turing Institute, London, UK

    Alejandro F. Frangi

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Contributions

A.D.-P. designed and executed all experiments, conducted all subsequent statistical analyses, and drafted the manuscript. N.R. helped design the experiments, helped with the writing, data interpretation and made substantial revisions and edits of the draft manuscript. R.A. helped design the experiments, contributed to the data analysis and data cleaning. A.S. and Y.Z. contributed to the data analysis. E.L., E.D., C.P.G., R.P.G. and S.P. contributed to the analysis of retinal and cardiac magnetic resonance images and shaped the medical contribution of this work. M.L. contributed to the design and implementation of the mcVAE. Q.C., T.D.L.K., E.A., E.Y.C. and Z.L. contributed to the external validation of the proposed method. A.F.F. conceived the methodology, helped design the experiments and contributed to the writing. All authors contributed to the manuscript.

Corresponding author

Correspondence to Alejandro F. Frangi.

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Extended data

Extended Data Fig. 1 Variance Comparison.

Comparison of the joint latent space variance: Obtained distribution of the latent variables when using the mcVAE on only retinal images and both retina + CMR.

Extended Data Fig. 2 FID.

Distribution of the Frechet Inception Distance (FID) score: Obtained distribution of the FID score on the reconstructed CMR images.

Extended Data Fig. 3 LinearRegression Coefficients.

Coefficients obtained from the logistic regression applied to myocardial infarction prediction using the estimated LVM/LVEDV and demographic data. Chol stands for cholesterol, bmi for body mass index, ads for alcohol daily consumption status, sbpa and dbpa stand for systolic and diastolic blood pressure, respectively.

Extended Data Fig. 4 Bland-Altman plots for different retinal image sizes.

Bland-Altman plots, correlation plots, and ROC curves for estimated LVM/LVEDV and MI prediction using different retinal image sizes. The solid line represents the logistic regression, and the dotted line represents the identity line.

Extended Data Fig. 5 Bland-Altman plots for different dataset sizes.

Bland-Altman plots, correlation plots, and ROC curves for estimated LVM/LVEDV and MI prediction using different dataset sizes. The solid line represents the logistic regression, and the dotted line represents the identity line.

Extended Data Fig. 6 Bland-Altman plots for estimated LVM/LVEDV using only retinal images.

Estimation of LVM and LVEDV using retinal images only or demographic data only: Bland-Altman and correlation plots for estimated LVM/LVEDV using only retinal images (upper plots) and only demographic data (lower plots). The solid line represents the logistic regression, and the dotted line represents the identity line.

Extended Data Fig. 7 ROC curves for MI prediction using different demographic data.

ROC curves for MI prediction using different demographic data: An eye clinic (that is age, gender). Accuracy: 0.71 ± 0.01, Sensitivity: 0.74 ± 0.03, Specificity: 0.73 ± 0.06, Precision: 0.68 ± 0.03, and F1 Score: 0.71 ± 0.01. ROC curves for MI prediction using demographic data that may be available at a cardiology department (that is Age, Gender, BMI, Diastolic BP, Systolic BP, HbA1c scores, Glucose, Cholesterol, Smoking and Drinking status). Accuracy: 0.72 ± 0.03, Sensitivity: 0.74 ± 0.02, Specificity: 0.70 ± 0.05, Precision: 0.70 ± 0.05, and F1 Score: 0.72 ± 0.03. The solid line represents the logistic regression, and the dotted line represents the identity line.

Extended Data Fig. 8 Coefficients and ROC curves for MI prediction obtained from the AREDS datasets.

Coefficients and ROC curves for MI prediction obtained from the AREDS datasets: (a) Coefficients obtained from the logistic regression applied to myocardial infarction prediction using the AREDS demographic data (sex, diastolic blood pressure (dbpa), systolic blood pressure (sbpa), smoking status (ss), body mass index (bmi) and age), and (b) ROC curve obtained for 10-fold cross-validation for MI predictions using only demographic data available in the AREDS dataset, and (c) ROC curve obtained for MI predictions using a logistic regression model trained on the UK Biobank and evaluated on the AREDS dataset. The solid line represents the logistic regression, and the dotted line represents the identity line.

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Supplementary Figs. 1–8, Tables 1–4 and Methods.

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Diaz-Pinto, A., Ravikumar, N., Attar, R. et al. Predicting myocardial infarction through retinal scans and minimal personal information. Nat Mach Intell 4, 55–61 (2022). https://doi.org/10.1038/s42256-021-00427-7

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