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Background <p>Cardiovascular disease (CVD) is the leading cause of mortality across the globe. Conventional risk stratification often depends on invasive biomarkers or generic risk scores that may underperform in key demographics, particularly women and middle-aged adults. Retinal microvasculature provides a non-invasive window into systemic vascular health (oculomics), but its integration with routine clinical vitals for sex-specific risk assessment remains underexplored.</p> Methods <p> We developed AVSeg-XAI, an interpretable DINOv2-based deep-learning pipeline for (i) artery/vein (A/V) segmentation and (ii) geometric/topological feature extraction from retinal fundus images. We then implemented a multimodal approach by integrating interpretable retinal vascular biomarkers and non-invasive clinical vitals into a Random Forest classifier to stratify CVD risk in a Qatar Biobank (QBB) cohort. Performance was assessed using stratified 10-fold cross-validation with subgroup analyses by sex and age. Biological plausibility was evaluated through virtual phenotyping, which predicts systemic traits based solely on retinal features.</p> Results <p>For artery/vein segmentation, the self-supervised AVSeg-XAI achieved a mean Dice score of 0.8368 on the benchmark dataset, exceeding the reported LUNet mean Dice score of 0.8327. Retinal feature extraction module yielded 34 refined vascular features encompassing caliber (CRAE, CRVE, AVR), fractal complexity, tortuosity, and branching topology; a parsimonious 7-feature interpretable subset was also defined for baseline comparison. Clinical inputs were restricted to 15 non-invasive variables obtainable without phlebotomy (e.g., age, BMI, blood pressure). The multimodal fusion model achieved an overall AUC of 0.81 (95% CI: 0.78–0.84). Stratified evaluation demonstrated superior performance in females (AUC 0.83, 95% CI: 0.79–0.87) compared with males (AUC 0.79, 95% CI: 0.75–0.83), with peak performance in the 45–55 year subgroup (AUC 0.90, 95% CI: 0.83–0.97).</p> Conclusion <p>We present an explainable, non-invasive CVD screening approach that integrates retinal imaging biomarkers with basic clinical vitals. We believe this study advances AI-powered oculomics as a proof-of-concept for community health screening, subject to prospective external validation.</p>

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AVSeg-XAI: Deep learning framework for A/V segmentation with vascular features reveals retinal oculomics as biomarker for cardiovascular disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月18日

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  • Syed Abdullah Basit,
  • Tanvir Alam

摘要

Background

Cardiovascular disease (CVD) is the leading cause of mortality across the globe. Conventional risk stratification often depends on invasive biomarkers or generic risk scores that may underperform in key demographics, particularly women and middle-aged adults. Retinal microvasculature provides a non-invasive window into systemic vascular health (oculomics), but its integration with routine clinical vitals for sex-specific risk assessment remains underexplored.

Methods

We developed AVSeg-XAI, an interpretable DINOv2-based deep-learning pipeline for (i) artery/vein (A/V) segmentation and (ii) geometric/topological feature extraction from retinal fundus images. We then implemented a multimodal approach by integrating interpretable retinal vascular biomarkers and non-invasive clinical vitals into a Random Forest classifier to stratify CVD risk in a Qatar Biobank (QBB) cohort. Performance was assessed using stratified 10-fold cross-validation with subgroup analyses by sex and age. Biological plausibility was evaluated through virtual phenotyping, which predicts systemic traits based solely on retinal features.

Results

For artery/vein segmentation, the self-supervised AVSeg-XAI achieved a mean Dice score of 0.8368 on the benchmark dataset, exceeding the reported LUNet mean Dice score of 0.8327. Retinal feature extraction module yielded 34 refined vascular features encompassing caliber (CRAE, CRVE, AVR), fractal complexity, tortuosity, and branching topology; a parsimonious 7-feature interpretable subset was also defined for baseline comparison. Clinical inputs were restricted to 15 non-invasive variables obtainable without phlebotomy (e.g., age, BMI, blood pressure). The multimodal fusion model achieved an overall AUC of 0.81 (95% CI: 0.78–0.84). Stratified evaluation demonstrated superior performance in females (AUC 0.83, 95% CI: 0.79–0.87) compared with males (AUC 0.79, 95% CI: 0.75–0.83), with peak performance in the 45–55 year subgroup (AUC 0.90, 95% CI: 0.83–0.97).

Conclusion

We present an explainable, non-invasive CVD screening approach that integrates retinal imaging biomarkers with basic clinical vitals. We believe this study advances AI-powered oculomics as a proof-of-concept for community health screening, subject to prospective external validation.