| Title: | Cross-modality Labeling Enables Noninvasive Capillary Quantification as a Sensitive Biomarker for Assessing Cardiovascular Risk |
| Journal: | Ophthalmology Science |
| Published: | 5 Dec 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38420613/ |
| DOI: | https://doi.org/10.1016/j.xops.2023.100441 |
| URL: | http://www.ophthalmologyscience.org/article/S2666914523001732/pdf |
Publication 9220
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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.</p>
Design: Cross-sectional and longitudinal study.</p>
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.</p>
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.</p>
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.</p>
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).</p>
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.</p>
Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.</p>
10 Authors
- Danli Shi
- Yukun Zhou
- Shuang He
- Siegfried K. Wagner
- Yu Huang
- Pearse A. Keane
- Daniel S.W. Ting
- Lei Zhang
- Yingfeng Zheng
- Mingguang He
Enabling scientific discoveries that improve human health