Predicting Atrial Fibrillation Onset from Fundus Images: Application of Multimodal Foundation Models in Cardiovascular Risk Early Warning

Original Author/Maintainer: YigePeng Source Platform: GitHub Original Title: CFP-RetiAF Original Link: https://github.com/YigePeng/CFP-RetiAF Source Publication/Update Time: 2026-05-27T13:57:29Z

Core of This Article: Introduces an innovative multimodal foundation model approach that predicts the risk of atrial fibrillation (AF) onset by analyzing fundus images combined with clinical metadata, demonstrating the great potential of medical imaging AI in early warning of cardiovascular diseases.

New Frontiers of Medical Imaging AI

Artificial intelligence has transformed the landscape of medical imaging diagnosis, but most applications focus on detecting existing lesions, with fewer studies predicting future disease risks.

Challenges of Atrial Fibrillation

AF is a common arrhythmia affecting tens of millions of people, prone to complications like stroke and heart failure. Moreover, paroxysmal AF is easily missed by traditional ECG, making early identification of high-risk groups crucial.

Hidden Link Between Fundus and Heart

The retina is the only part where blood vessels and nerves can be directly observed. Its vascular morphological features (diameter, fractal dimension, etc.) are closely related to cardiovascular health, providing a basis for predicting cardiovascular events via fundus images.

Multimodal Foundation Model Architecture

The core is a multimodal model that processes both fundus images (high-resolution color photos) and clinical metadata (age, gender, blood pressure, medical history, etc.) simultaneously.

Visual Encoder

Uses pre-trained visual foundation models (such as CNN or Vision Transformer) to extract hierarchical features from fundus images and identify subtle vascular changes.

Cross-Modal Fusion

Explores early, middle, and late fusion strategies; experiments show that well-designed fusion can significantly improve performance.

Temporal Modeling

Adopts survival analysis methods to predict whether AF will occur and the time window of occurrence.

Dataset Features

Based on a large-scale longitudinal cohort, including fundus images and long-term follow-up records of tens of thousands of subjects. Features: long follow-up period (over 10 years), precise event annotation, case-control matching.

Validation Strategies

• Time-stratified cross-validation: Avoids time overlap
• External validation: Validates generalization ability using data from independent medical centers
• Subgroup analysis: Evaluates consistency of performance across different populations

Core Findings

• Predictive performance: Distinguishes high-risk/low-risk groups; accuracy improves with longer follow-up; fundus information provides additional predictive value
• Interpretability: Arteriolar stenosis, vascular tortuosity, and optic disc features are associated with AF risk
• Multimodal gain: Outperforms single-modal models

Development of Screening Tools

Fundus photography is non-invasive, fast, and low-cost, making it suitable as an AF screening tool for primary care, health check-ups, and monitoring of high-risk groups.

Guidance for Early Intervention

After identifying high-risk groups, the following can be taken:

• Lifestyle interventions (blood pressure and blood glucose control)
• Anticoagulation therapy assessment (for extremely high-risk patients)
• Rhythm monitoring (more frequent monitoring)

Precision Medicine Applications

• Risk stratification: Subdivides populations for stratified management
• Decision support: Assists clinicians
• Prognosis evaluation: Assesses intervention effects

Image Quality Dependence

Model performance is affected by blurred images, improper exposure, and incomplete field of view; supporting quality control mechanisms are needed.

Ethnic and Device Differences

There are differences in fundus features among different ethnic groups and images taken by different devices; generalization ability needs continuous improvement.

Limitations of Causal Inference

The model identifies statistical correlations rather than causal mechanisms; further research is needed to clarify the causal relationship between fundus features and AF.

Joint Prediction of Multiple Diseases

Future models are expected to predict the risk of multiple diseases such as stroke, myocardial infarction, and cognitive impairment simultaneously, enabling comprehensive health assessment.

Continuous Monitoring and Dynamic Evaluation

Regular fundus examinations track individual risk changes; dynamic evaluation is more clinically valuable than single prediction.

Fusion with Other Modalities

Combine with fine imaging modalities like OCT and fundus fluorescein angiography to improve predictive performance.