Maldonado García, C. orcid.org/0000-0002-3179-7070, Bonazzola, R. orcid.org/0000-0001-8811-2581, Ravikumar, N. orcid.org/0000-0003-0134-107X et al. (1 more author) (2022) Predicting Myocardial Infarction Using Retinal OCT Imaging. In: Medical Image Understanding and Analysis 26th Annual Conference, MIUA 2022, 27-29 Jul 2022, Cambridge, UK.
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 ± 0.01, accuracy 0.674 ± 0.007, precision 0.657 ± 0.012, recall 0.678 ± 0.017 and f1-score 0.653 ± 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.
Metadata
Item Type: | Conference or Workshop Item |
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Authors/Creators: |
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Keywords: | Retinal optical coherence tomography; Variational autoencoder; Myocardial infarction |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Sep 2023 12:37 |
Last Modified: | 04 Sep 2023 12:37 |
Status: | Published |
Publisher: | Springer International Publishing |
Identification Number: | 10.1007/978-3-031-12053-4_58 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202915 |