Using Artificial Intelligence to Detect Cardiovascular Disease through Retinal Imaging: A Review
DOI:
https://doi.org/10.61173/w6jd1f47Keywords:
Artificial Intelligence, Retinal Imaging, Cardiovascular Disease, Risk PredictionAbstract
Cardiovascular diseases cause a large number of deaths and disabilities worldwide. To reduce the related losses and burdens, it is especially important to promote early non-invasive screening methods. In recent years, with the development of AI, retinal images can become a more important tool in cardiovascular risk assessment. This article reviews the application of artificial intelligence in the screening of cardiovascular diseases using retinal images and finds that AI can accurately predict cardiovascular disease risks from retinal images and can achieve better performance by integrating multimodal data and using large-scale databases. However, the heterogeneity of data, cross-population applicability and ethical issues still limit the discoveries in this field. In the future, larger-scale model training, multi-task learning and different-level data fusion can be used to improve the predictive ability and accuracy of the model. At the same time, this method has potential in remote medical care, personalized medical care, and public health fields.
References
[1] Ghenciu, L. A., Dima, M., Stoicescu, E. R., Iacob, R., Boru, C., & Hațegan, O. A. (2024). Retinal Imaging-Based Oculomics: Artificial intelligence as a tool in the diagnosis of cardiovascular and metabolic diseases. Biomedicines, 12(9), 2150. https://doi. org/10.3390/biomedicines12092150
[2] Zhang, L., Yuan, M., An, Z., Zhao, X., Wu, H., Li, H., Wang, Y., Sun, B., Li, H., Ding, S., Zeng, X., Chao, L., Li, P., & Wu, Dean&Francis ISSN 2959-409X W. (2020). Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China. PLoS ONE, 15(5), e0233166. https://doi.org/10.1371/journal. pone.0233166
[3] Rudnicka, A. R., Welikala, R., Barman, S., Foster, P. J., Luben, R., Hayat, S., Khaw, K., Whincup, P., Strachan, D., & Owen, C. G. (2022). Artificial intelligence-enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke. British Journal of Ophthalmology, 106(12), 1722–1729. https://doi.org/10.1136/bjo-2022-321842
[4] Vujosevic, S., Aldington, S. J., Silva, P., Hernández, C., Scanlon, P., Peto, T., & Simó, R. (2020). Screening for diabetic retinopathy: new perspectives and challenges. The Lancet Diabetes & Endocrinology, 8(4), 337–347. https://doi. org/10.1016/s2213-8587(19)30411-5
[5] Tan, T., & Wong, T. Y. (2023). Diabetic retinopathy: Looking forward to 2030. Frontiers in Endocrinology, 13. https://doi. org/10.3389/fendo.2022.1077669
[6] Dong, L., He, W., Zhang, R., Ge, Z., Wang, Y. X., Zhou, J., Xu, J., Shao, L., Wang, Q., Yan, Y., Xie, Y., Fang, L., Wang, H., Wang, Y., Zhu, X., Wang, J., Zhang, C., Wang, H., Wang, Y., . . . Wei, W. B. (2022). Artificial intelligence for screening of multiple retinal and optic nerve diseases. JAMA Network Open, 5(5), e229960. https://doi.org/10.1001/ jamanetworkopen.2022.9960
[7] Varela, M. D., Sen, S., De Guimaraes, T. a. C., Kabiri, N., Pontikos, N., Balaskas, K., & Michaelides, M. (2023). Artificial intelligence in retinal disease: clinical application, challenges, and future directions. Graefe S Archive for Clinical and Experimental Ophthalmology, 261(11), 3283–3297. https://doi. org/10.1007/s00417-023-06052-x
[8] Rim, T. H., Lee, G., Kim, Y., Tham, Y., Lee, C. J., Baik, S. J., Kim, Y. A., Yu, M., Deshmukh, M., Lee, B. K., Park, S., Kim, H. C., Sabayanagam, C., Ting, D. S. W., Wang, Y. X., Jonas, J. B., Kim, S. S., Wong, T. Y., & Cheng, C. (2020). Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms. The Lancet Digital Health, 2(10), e526–e536. https://doi.org/10.1016/s2589- 7500(20)30216-8
[9] Hu, W., Yii, F. S. L., Chen, R., Zhang, X., Shang, X., Kiburg, K., Woods, E., Vingrys, A., Zhang, L., Zhu, Z., & He, M. (2023). A Systematic Review and Meta-Analysis of applying Deep learning in the prediction of the risk of cardiovascular diseases from Retinal Images. Translational Vision Science & Technology, 12(7), 14. https://doi.org/10.1167/tvst.12.7.14
[10] Lee, Y. C., Cha, J., Shim, I., Park, W., Kang, S. W., Lim, D. H., & Won, H. (2023). Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction. Npj Digital Medicine, 6(1). https://doi.org/10.1038/ s41746-023-00748-4
[11] Hu, W., Lin, Z., Clark, M., Henwood, J., Shang, X., Chen, R., Kiburg, K., Zhang, L., Ge, Z., Van Wijngaarden, P., Zhu, Z., & He, M. (2025). Real-world feasibility, accuracy and acceptability of automated retinal photography and AI-based cardiovascular disease risk assessment in Australian primary care settings: a pragmatic trial. Npj Digital Medicine, 8(1). https://doi. org/10.1038/s41746-025-01436-1
[12] Wang, J., Wang, Y. X., Zeng, D., Zhu, Z., Li, D., Liu, Y., Sheng, B., Grzybowski, A., & Wong, T. Y. (2025). Artificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases. Theranostics, 15(8), 3223–3233. https://doi. org/10.7150/thno.100786
[13] Häner, N. U., Dysli, C., & Munk, M. R. (2023). Imaging in retinal vascular disease: A review. Clinical and Experimental Ophthalmology, 51(3), 217–228. https://doi.org/10.1111/ ceo.14203
[14] Veritti, D., Rubinato, L., Sarao, V., De Nardin, A., Foresti, G. L., & Lanzetta, P. (2023). Behind the mask: a critical perspective on the ethical, moral, and legal implications of AI in ophthalmology. Graefe S Archive for Clinical and Experimental Ophthalmology, 262(3), 975–982. https://doi.org/10.1007/ s00417-023-06245-4
[15] An, S., Teo, K., McConnell, M. V., Marshall, J., Galloway, C., & Squirrell, D. (2025). AI explainability in oculomics: how it works, its role in establishing trust, and what still needs to be addressed. Progress in Retinal and Eye Research, 101352. https://doi.org/10.1016/j.preteyeres.2025.101352
[16] Huang, Y., Cheung, C. Y., Li, D., Tham, Y. C., Sheng, B., Cheng, C. Y., Wang, Y. X., & Wong, T. Y. (2023). AI- integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook. Eye, 38(3), 464–472. https:// doi.org/10.1038/s41433-023-02724-4
[17] Arnould, L., Meriaudeau, F., Guenancia, C., Germanese, C., Delcourt, C., Kawasaki, R., Cheung, C. Y., Creuzot-Garcher, C., & Grzybowski, A. (2022). Using artificial intelligence to analyse the retinal vascular network: The future of cardiovascular risk Assessment based on Oculomics? A Narrative review. Ophthalmology and Therapy, 12(2), 657–674. https://doi. org/10.1007/s40123-022-00641-5
[18] Olawade, D. B., Weerasinghe, K., Mathugamage, M. D. D. E., Odetayo, A., Aderinto, N., Teke, J., & Boussios, S. (2025). Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence. Medicina, 61(3), 433. https://doi.org/10.3390/ medicina61030433
[19] Rane, N., Choudhary, S., & Rane, J. (2023). Explainable Artificial Intelligence (XAI) in healthcare: Interpretable Models for Clinical Decision Support. SSRN Electronic Journal. https:// doi.org/10.2139/ssrn.4637897
[20] Khokhar, P. B., Gravino, C., & Palomba, F. (2025). Advances in artificial intelligence for diabetes prediction: insights from a systematic literature review. Artificial Intelligence in Medicine, 103132. https://doi.org/10.1016/ j.artmed.2025.103132 Dean&Francis Xuesong Han
[21] Chew, E. Y., Burns, S. A., Abraham, A. G., Bakhoum, M. F., Beckman, J. A., Chui, T. Y. P., Finger, R. P., Frangi, A. F., Gottesman, R. F., Grant, M. B., Hanssen, H., Lee, C. S., Meyer, M. L., Rizzoni, D., Rudnicka, A. R., Schuman, J. S., Seidelmann, S. B., Tang, W. H. W., Adhikari, B. B., . . . Oh, Y. S. (2024). Standardization and clinical applications of retinal imaging biomarkers for cardiovascular disease: a Roadmap from an NHLBI workshop. Nature Reviews Cardiology, 22(1), 47–63. https://doi.org/10.1038/s41569-024-01060-8
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