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Dynamic Facial Analysis for Predicting Facial Palsy Outcomes: Comparing Landmark Detection Models and Integrating

Akshita A Rao, Jacqueline J Greene, Todd P Coleman

    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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    Modern facial landmark detection and ordinal regression improve facial nerve recovery prediction in facial palsy patients. These advanced methods offer better accuracy and interpretability than previous approaches for clinical assessment.

    Area of Science:

    • Medical imaging analysis
    • Computational neuroscience
    • Clinical assessment tools

    Background:

    • Facial palsy significantly impacts quality of life, necessitating accurate monitoring of facial nerve (FN) recovery.
    • Previous predictive frameworks for FN recovery relied on conventional methods with limitations in precision.
    • Video-based assessment offers a non-invasive approach to track recovery, but requires robust analytical tools.

    Purpose of the Study:

    • To enhance video-based prediction and assessment of facial nerve recovery in facial palsy.
    • To evaluate the efficacy of modern landmark detection models and ordinal regression techniques.
    • To compare these advanced methods against previously reported predictive frameworks and conventional approaches.

    Main Methods:

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  • Comparison of state-of-the-art facial landmark detection models (including deep learning) with Dlib for accuracy and computational cost.
  • Implementation of ordinal regression, utilizing Wasserstein and Mahalanobis distances, to predict House-Brackmann (HB) scores.
  • Evaluation of the impact of different landmark detection models and regression techniques on clinical score predictions.

    • Main Results:

    • Dlib provided an optimal balance between computational efficiency and clinical accuracy for landmark detection.
    • Advanced high-resolution models did not yield significant improvements in predicting clinical scores compared to Dlib.
    • Ordinal regression demonstrated superior performance, interpretability, and reduced mean absolute error over naive linear regression for HB score prediction.

    Conclusions:

    • Modern landmark detection and ordinal regression significantly enhance the prediction accuracy for facial nerve recovery in facial palsy.
    • The refined framework offers a more robust and clinically relevant tool for monitoring patient progress and aiding surgical decisions.
    • This study bridges the gap between computational modeling and clinical application, improving precision in facial palsy assessment.