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Updated: Jan 9, 2026

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Multimodal Deep Learning for Stroke Prediction and Detection using Retinal Imaging and Clinical Data.

Saeed Shurrab, Aadim Nepal, Terrence J Lee-St John

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
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    Retinal imaging combined with clinical data shows promise for stroke detection and risk prediction. This cost-effective deep learning approach improves accuracy over image-only methods, aiding early intervention.

    Area of Science:

    • Neuroscience
    • Ophthalmology
    • Artificial Intelligence

    Background:

    • Stroke is a significant global health issue.
    • Current stroke diagnosis relies on expensive medical imaging.
    • Retinal imaging offers a potential cost-effective alternative for assessing cerebrovascular health.

    Purpose of the Study:

    • To investigate the use of retinal images and clinical data for stroke detection and risk prediction.
    • To develop and evaluate a multimodal deep neural network for this purpose.

    Main Methods:

    • A multimodal deep neural network was developed, processing Optical Coherence Tomography (OCT) and infrared reflectance retinal scans.
    • The model integrated clinical data including demographics, vital signs, and diagnosis codes.
    • Self-supervised learning was used for pretraining on a large dataset, followed by fine-tuning and evaluation on a labeled subset.

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    Main Results:

    • The multimodal model demonstrated effectiveness in detecting retinal changes associated with acute stroke.
    • It accurately predicted future stroke risk within a specified time horizon.
    • Achieved a 5% AUROC improvement over unimodal image baselines and an 8% improvement over a state-of-the-art foundation model.

    Conclusions:

    • Retinal imaging, when combined with clinical data, holds significant potential for identifying high-risk stroke patients.
    • The proposed deep learning framework offers a non-invasive and cost-effective method for stroke risk assessment.
    • This approach can contribute to early intervention, mitigating the global stroke burden and improving patient outcomes.