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AVSeg-XAI: Deep learning framework for A/V segmentation with vascular features reveals retinal oculomics as biomarker
Syed Abdullah Basit1, Tanvir Alam2
1College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Insights
This study introduces an AI-driven method using retinal images and basic vitals to non-invasively assess cardiovascular disease (CVD) risk, showing promise for early detection, especially in women.
Area of Science:
- Oculomics and AI in Healthcare
- Biomedical Engineering
- Cardiovascular Disease Research
Background:
- Cardiovascular disease (CVD) is a leading global cause of death.
- Traditional risk assessment methods may underperform in specific demographics like women and middle-aged adults.
- Retinal microvasculature offers a non-invasive indicator of systemic vascular health.
Purpose of the Study:
- To develop and validate an interpretable deep-learning pipeline (AVSeg-XAI) for retinal vascular analysis.
- To integrate retinal biomarkers with non-invasive clinical vitals for improved CVD risk stratification.
- To assess the performance of this multimodal approach across different sexes and age groups.
Main Methods:
- Developed AVSeg-XAI for artery/vein segmentation and feature extraction from retinal images.
- Implemented a multimodal Random Forest classifier combining retinal features and 15 non-invasive clinical variables.
- Utilized a Qatar Biobank cohort with 10-fold cross-validation and subgroup analyses by sex and age.
Main Results:
- AVSeg-XAI achieved high accuracy in retinal artery/vein segmentation (Dice score 0.8368).
- The multimodal model demonstrated an overall AUC of 0.81 for CVD risk stratification.
- Superior performance was observed in females (AUC 0.83) and the 45-55-year subgroup (AUC 0.90).
Conclusions:
- An explainable, non-invasive AI-powered approach for CVD screening was developed.
- This study advances AI-driven oculomics as a viable tool for community health screening.
- Prospective external validation is recommended for broader clinical application.
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.
