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Multimodal Machine Learning for Stroke Prognosis and Diagnosis: A Systematic Review.

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    Multimodal machine learning shows promise for stroke diagnosis and prognosis by integrating diverse data. This review highlights fusion techniques and suggests exploring new methods for better patient outcomes.

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    Area of Science:

    • Neurology
    • Artificial Intelligence
    • Medical Informatics

    Background:

    • Stroke poses significant mortality and sensorimotor deficit risks.
    • Machine learning (ML) is increasingly used for stroke outcome prediction.
    • Multimodal ML is gaining traction due to diverse clinical data types (images, bio-signals, clinical data).

    Approach:

    • Systematic literature review following PRISMA guidelines.
    • Focused on state-of-the-art multimodal ML methods for stroke prognosis and diagnosis.
    • Analyzed dominant fusion paradigms (early, joint, late) and less explored methods (translation, alignment).

    Key Points:

    • Fusion techniques (early, joint, late) dominate current multimodal ML for stroke.
    • Opportunities exist in exploring multimodal translation and alignment.
    • Current datasets often lack diversity in scale and modality types.

    Conclusions:

    • Advancements in multimodal ML are crucial for improving stroke diagnosis and prognosis.
    • Developing more diverse multimodal datasets is essential.
    • Further research into novel multimodal learning paradigms is recommended.