Abstract
Late-stage identification of patients at risk of myocardial infarction (MI) inhibits delivery of effective preventive care, increasing the burden on healthcare services and affecting patients’ quality of life. Hence, standardised non-invasive, accessible, and low-cost methods for early identification of patient’s at risk of future MI events are desirable. In this study, we demonstrate for the first time that retinal optical coherence tomography (OCT) imaging can be used to identify future adverse cardiac events such as MI. We propose a binary classification network based on a task-aware Variational Autoencoder (VAE), which learns a latent embedding of patients’ OCT images and uses the former to classify the latter into one of two groups, i.e. whether they are likely to have a heart attack (MI) in the future or not. Results obtained for experiments conducted in this study (AUROC \(0.74 \pm 0.01\), accuracy \(0.674 \pm 0.007\), precision \(0.657 \pm 0.012\), recall \(0.678 \pm 0.017\) and f1-score \(0.653 \pm 0.013\)) demonstrate that our task-aware VAE-based classifier is superior to standard convolution neural network classifiers at identifying patients at risk of future MI events based on their retinal OCT images. This proof-of-concept study indicates that retinal OCT imaging could be used as a low-cost alternative to cardiac magnetic resonance imaging, for identifying patients at risk of MI early.
N. Ravikumar and A.F. Frangi—Joint last authors.
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References
Lui Cheung, et al.: A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nat. Biomed. Eng. (2020)
Consortium, M.: Monai: medical open network for AI, February 2022. https://doi.org/10.5281/zenodo.6114127. If you use this software, please cite it using these metadata
D’Agostino, R.B., et al.: General cardiovascular risk profile for use in primary care: the Framingham heart study. Circulation 117, 743–753 (2008)
Diaz-Pinto, A., et al.: Predicting myocardial infarction through retinal scans and minimal personal information. Nat. Mach. Intell. 4, 55–61 (2022)
Farrah, T.E., Webb, D.J., Dhaun, N.: Retinal fingerprints for precision profiling of cardiovascular risk. Nat. Rev. Cardiol. 16, 379–381 (2019)
Fu, H., et al.: Evaluation of retinal image quality assessment networks in different color-spaces. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 48–56. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_6
Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2017)
Kaptoge, S., et al.: World health organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob. Health 7(10), e1332–e1345 (2019)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. CoRR abs/1412.6980 (2015)
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. CoRR abs/1312.6114 (2014)
Littlejohns, T.J., Sudlow, C.L.M., Allen, N.E., Collins, R.: UK biobank: opportunities for cardiovascular research. Eur. Heart J. 40, 1158–1166 (2019)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: NeurIPS (2019)
Poplin, R., et al.: Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2, 158–164 (2018)
Ruwanpathirana, T., Owen, A.J., Reid, C.M.: Review on cardiovascular risk prediction. Cardiovasc. Ther. 33(2), 62–70 (2015)
Sandoval-Garcia, E., et al.: Retinal arteriolar tortuosity and fractal dimension are associated with long-term cardiovascular outcomes in people with type 2 diabetes. Diabetologia 64(10), 2215–2227 (2021). https://doi.org/10.1007/s00125-021-05499-z
Son, J., Shin, J.Y., Chun, E.J., Jung, K.H., Park, K.H., Park, S.J.: Predicting high coronary artery calcium score from retinal fundus images with deep learning algorithms. Transl. Vision Sci. Technol. 9 (2020)
Acknowledgements
This research was conducted using data from the UK Biobank under access application 11350. AFF is funded by the Royal Academy of Engineering (INSILEX CiET181919), Engineering and Physical Sciences Research Council (EPSRC) programs TUSCA EP/V04799X/1, and the Royal Society Exchange Programme CROSSLINK IESNSFC201380. CMG is funded by Consejo Nacional de Ciencia y Tecnología-CONACyT (scholarship no. 766588).
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Maldonado García, C., Bonazzola, R., Ravikumar, N., Frangi, A.F. (2022). Predicting Myocardial Infarction Using Retinal OCT Imaging. In: Yang, G., Aviles-Rivero, A., Roberts, M., Schönlieb, CB. (eds) Medical Image Understanding and Analysis. MIUA 2022. Lecture Notes in Computer Science, vol 13413. Springer, Cham. https://doi.org/10.1007/978-3-031-12053-4_58
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