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

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.

Figure 1

Workflow of the study. AVR = arteriolar-to-venular ratio; CF = color fundus; CVD = cardiovascular disease; FFA = fundus fluorescein angiography; RMHAS = Retina-based Microvascular Health Assessment System; VSD = vessel skeleton density.

Figure 2

Overlay of registered vessels extracted from color fundus (CF) photography and fundus fluorescein angiography (FFA). First column: CF image, second column: FFA image, third column: CF vessels in green, FFA vessels in red, intersection of CF and FFA vessels in yellow. The vessel trunks overlay well, the FFA image clearly identifies more capillaries than CF, and the registered FFA vessels will be used as labels to train a deep learning model to segment retinal vessels, including capillaries from CF.

Figure 3

Demonstration of color fundus photograph, fundus fluorescein angiography (FFA), pseudolabels extracted from FFA, manual segmentation from the FundusCapi dataset, and Retina-based Microvascular Health Assessment System with fluorescein angiography model prediction (based on color fundus).

Figure 4

Association of retinal vessel measurements with incident cardiovascular disease (CVD) in the UK Biobank study. A, Female participants; B, Male participants. Pink denotes artery, blue denotes vein. Model 1 unadjusted, Model 2 adjusted for age, Model 3 adjusted for age, systolic blood pressure, diastolic blood pressure, body mass index, smoking status, blood cholesterol, and diabetes. AVR = artery to vein ratio; CI = confidence interval; HR = hazard ratio; VSD = vessel skeleton density (in pixel unit).

Figure 5

Association of retinal vessel measurements in different retinal region (macular region and other region) with incident cardiovascular disease (CVD) in the UK Biobank study. A, Female participants; B, Male participants. Pink denotes artery, blue denotes vein. Model 1 unadjusted, Model 2 adjusted for age, Model 3 adjusted for age, systolic blood pressure, diastolic blood pressure, body mass index, smoking status, blood cholesterol and diabetes. CI = confidence interval; DD = 1 disc diameter from the fovea; HR = hazard ratio; VSD = vessel skeleton density.