International Conference onOtology and Neurotology

Advances in Machine Learning for Neuro-Otology Diagnosis

Recent research has highlighted the potential of machine learning (ML) to enhance diagnostic accuracy in neuro-otology. ML models have been developed to analyze complex clinical data, including patient history, examination findings, and vestibular function tests, to differentiate between various causes of vertigo. These models aim to assist clinicians in real-time diagnosis, especially in distinguishing conditions like stroke from vestibular neuritis. While promising, further validation in diverse populations is necessary to ensure their reliability in clinical settings.