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Video-Based Facial Weakness Analysis.

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    This study introduces a novel framework for detecting facial weakness, achieving high accuracy comparable to medical professionals. This technology can empower non-neurologists to identify facial weakness in the field, enabling earlier treatment.

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    Area of Science:

    • Neurology
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Facial weakness is a common indicator of neurological conditions like Bell's palsy and stroke.
    • Accurate detection of facial weakness typically requires specialized neurological training and experience.
    • Current diagnostic methods can be a barrier in non-clinical settings.

    Purpose of the Study:

    • To develop and evaluate an automated framework for detecting facial weakness.
    • To model temporal dynamics of facial shape and appearance features using deep learning.
    • To assess the feasibility of using this technology for early detection by non-specialists.

    Main Methods:

    • A bi-directional long short-term memory (Bi-LSTM) network was employed to analyze temporal dynamics of facial features.
    • The framework was evaluated on an "in-the-wild" video dataset.
    • Performance was benchmarked against other algorithms and rated by neurologists, EMS personnel, and residents.

    Main Results:

    • The proposed algorithm achieved high performance metrics: 94.3% accuracy, 91.4% sensitivity, and 95.7% specificity.
    • The system demonstrated performance on par with paramedics and outperformed other comparison methods.
    • The framework offers visualizable and interpretable results, enhancing model transparency.

    Conclusions:

    • The developed framework effectively identifies facial weakness.
    • This proof-of-concept demonstrates the potential for non-neurologists to utilize this technology for field-based detection.
    • Early identification can lead to increased coverage and timely medical intervention.