Cross-modality Labeling Enables Noninvasive Capillary Quantification as a Sensitive Biomarker for Assessing Cardiovascular Risk

Danli Shi; Yukun Zhou; Shuang He; Siegfried K Wagner; Yu Huang; Pearse A Keane; Daniel S W Ting; Lei Zhang; Yingfeng Zheng; Mingguang He

doi:10.1016/j.xops.2023.100441

Cross-modality Labeling Enables Noninvasive Capillary Quantification as a Sensitive Biomarker for Assessing Cardiovascular Risk

Ophthalmol Sci. 2023 Dec 5;4(3):100441. doi: 10.1016/j.xops.2023.100441. eCollection 2024 May-Jun.

Authors

Danli Shi^{1

2

3}, Yukun Zhou⁴, Shuang He³, Siegfried K Wagner⁵, Yu Huang⁶, Pearse A Keane⁵, Daniel S W Ting⁷, Lei Zhang⁸, Yingfeng Zheng³, Mingguang He^{1

2

6}

Affiliations

¹ School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China.
² Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China.
³ State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
⁴ Centre for Medical Image Computing, University College London, London, UK.
⁵ NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.
⁶ Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China.
⁷ Singapore National Eye Center, Singapore Eye Research Institute, and Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.
⁸ Faculty of Medicine, Central Clinical School, Monash University, Melbourne, Victoria, Australia.

Abstract

Purpose: We aim to use fundus fluorescein angiography (FFA) to label the capillaries on color fundus (CF) photographs and train a deep learning model to quantify retinal capillaries noninvasively from CF and apply it to cardiovascular disease (CVD) risk assessment.

Design: Cross-sectional and longitudinal study.

Participants: A total of 90732 pairs of CF-FFA images from 3893 participants for segmentation model development, and 49229 participants in the UK Biobank for association analysis.

Methods: We matched the vessels extracted from FFA and CF, and used vessels from FFA as labels to train a deep learning model (RMHAS-FA) to segment retinal capillaries using CF. We tested the model's accuracy on a manually labeled internal test set (FundusCapi). For external validation, we tested the segmentation model on 7 vessel segmentation datasets, and investigated the clinical value of the segmented vessels in predicting CVD events in the UK Biobank.

Main outcome measures: Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity for segmentation. Hazard ratio (HR; 95% confidence interval [CI]) for Cox regression analysis.

Results: On the FundusCapi dataset, the segmentation performance was AUC = 0.95, accuracy = 0.94, sensitivity = 0.90, and specificity = 0.93. Smaller vessel skeleton density had a stronger correlation with CVD risk factors and incidence (P < 0.01). Reduced density of small vessel skeletons was strongly associated with an increased risk of CVD incidence and mortality for women (HR [95% CI] = 0.91 [0.84-0.98] and 0.68 [0.54-0.86], respectively).

Conclusions: Using paired CF-FFA images, we automated the laborious manual labeling process and enabled noninvasive capillary quantification from CF, supporting its potential as a sensitive screening method for identifying individuals at high risk of future CVD events.

Financial disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Keywords: Cardiovascular disease; Cross-modality labeling; RMHAS-FA; Retinal capillary quantification.

Grants and funding

MR/T019050/1/MRC_/Medical Research Council/United Kingdom