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Deep Learning Detection of Facial Nerve Enhancement in Bell's Palsy
Habip Eser Akkaya1, Önder Polat2, Nur Hürsoy3
1Department of Radiology, Ankara Training and Research Hospital, Ankara, Türkiye.
Annals of Indian Academy of Neurology
|June 16, 2026
Summary
Machine learning accurately identified facial nerve enhancement in Bell's palsy (BP) patients using MRI. This AI tool achieved 80% accuracy, aiding in diagnosing this common cause of facial weakness.
Area of Science:
- Neuroimaging
- Artificial Intelligence in Medicine
- Cranial Nerve Disorders
Background:
- The facial nerve (seventh cranial nerve) is crucial for facial expression and function.
- Bell's palsy, a common idiopathic condition, causes unilateral facial weakness.
- MRI is vital for diagnosing Bell's palsy by detecting facial nerve inflammation, but physiological enhancement can complicate interpretation.
Purpose of the Study:
- To assess facial nerve enhancement in Bell's palsy patients.
- To develop and evaluate a machine learning algorithm for identifying pathological facial nerve enhancement.
Main Methods:
- Retrospective analysis of 306 MRI scans (156 Bell's palsy patients, 150 controls).
- Development and training of a DenseNet-based deep learning model to detect pathological enhancement.
- Model performance evaluated against neuroradiologist diagnoses.
Main Results:
- The deep learning model achieved 80% accuracy.
- The model demonstrated an area under the curve (AUC) of 87%.
- Results indicate the model's potential to assist clinicians in diagnosing facial nerve pathologies.
Conclusions:
- AI-based assessment tools show promise for evaluating facial nerve pathologies.
- Further validation with larger datasets is recommended to enhance clinical applicability.
- Future research should focus on external validation of AI models for facial nerve assessment.
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