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  • Review Article
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AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook

Eye volume 38, pages 464–472 (2024)Cite this article

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Abstract

Cardiovascular disease (CVD) remains the leading cause of death worldwide. Assessing of CVD risk plays an essential role in identifying individuals at higher risk and enables the implementation of targeted intervention strategies, leading to improved CVD prevalence reduction and patient survival rates. The ocular vasculature, particularly the retinal vasculature, has emerged as a potential means for CVD risk stratification due to its anatomical similarities and physiological characteristics shared with other vital organs, such as the brain and heart. The integration of artificial intelligence (AI) into ocular imaging has the potential to overcome limitations associated with traditional semi-automated image analysis, including inefficiency and manual measurement errors. Furthermore, AI techniques may uncover novel and subtle features that contribute to the identification of ocular biomarkers associated with CVD. This review provides a comprehensive overview of advancements made in AI-based ocular image analysis for predicting CVD, including the prediction of CVD risk factors, the replacement of traditional CVD biomarkers (e.g., CT-scan measured coronary artery calcium score), and the prediction of symptomatic CVD events. The review covers a range of ocular imaging modalities, including colour fundus photography, optical coherence tomography, and optical coherence tomography angiography, and other types of images like external eye images. Additionally, the review addresses the current limitations of AI research in this field and discusses the challenges associated with translating AI algorithms into clinical practice.

摘要

心血管疾病(CVD)仍然是全球死亡的主要原因。评估CVD的风险在识别高风险个体方面发挥着重要作用, 并能够实施有针对性的干预策略, 从而降低CVD患病率并提高患者生存率。眼部血管, 特别是视网膜血管, 由于其与大脑和心脏等重要器官的解剖学和生理特征的相似性, 已成为CVD风险分层的潜在手段。将人工智能(AI)应用于眼科成像有可能克服传统半自动图像分析的局限性, 包括低效率和人工测量误差。此外, 人工智能技术有助于识别与CVD相关的眼部生物标志物的细微特征。本综述全面概述了基于AI的眼部图像分析在预测CVD方面取得的进展, 包括预测CVD 危险因素、代替传统CVD生物标志物(例如CT扫描测量的冠状动脉钙指标)以及预测症状性CVD疾病。本综述涵盖了一系列的眼部成像方式, 包括彩色眼底照相、光学相干断层扫描和光学相干断层扫描血管造影, 以及其他类型的图像, 如眼外部照相。此外, 综述提出了目前在该领域人工智能研究的局限性, 并讨论了将人工智能算法转化为临床实践的相关挑战。

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Fig. 1: The schematic diagram depicts the implementation of deep learning (DL) techniques to aid in the prediction of cardiovascular diseases (CVD) through the analysis of ocular images.

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Funding

The work was supported by National Key Research and Development Program of China (No. 2022YFC2502800).

Author information

Author notes

  1. These authors contributed equally: Ya Xing Wang, Tien Yin Wong.

Authors and Affiliations

  1. Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China

    Yu Huang & Ya Xing Wang

  2. Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China

    Carol Y Cheung

  3. College of Future Technology, Peking University, Beijing, China

    Dawei Li

  4. Centre for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Yih Chung Tham & Ching Yu Cheng

  5. Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore

    Yih Chung Tham, Ching Yu Cheng & Tien Yin Wong

  6. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  7. Tsinghua Medicine, Tsinghua University, Beijing, China

    Tien Yin Wong

  8. School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China

    Tien Yin Wong

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  7. Ya Xing Wang
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  8. Tien Yin Wong
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Contributions

YH: manuscript writing; CYC (Carol Y Cheung): Concept and paper revision; DWL: Literature review and paper revision; YCT: Literature review and paper revision; BS: Literature review and paper revision; CYC (Ching Yu Cheng): Literature review and paper revision; YXW: Writing and paper revision; TYW: Concept, original presentation and revision. The corresponding authors have listed all authors who met the authorship criteria into the manuscript.

Corresponding author

Correspondence to Tien Yin Wong.

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Competing interests

TYW is a consultant for Aldropika Therapeutics, Bayer, Boehringer-Ingelheim, Genentech, Iveric Bio, Novartis, Oxurion, Plano, Roche, Sanofi, Shanghai Henlius. He is an inventor, holds patents and is a co-founder of start-up companies EyRiS and Visre, which have interests in, and develop digital solutions for eye diseases.

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Huang, Y., Cheung, C.Y., Li, D. et al. AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook. Eye 38, 464–472 (2024). https://doi.org/10.1038/s41433-023-02724-4

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