<![CDATA[Vertigo is a prevalent symptom of vestibular disorders, with ocular nystagmus analysis serving as a key indicator for distinguishing between peripheral and central vestibular conditions. Videonystagmography (VNG) provides objective and reliable measurements, making it a valuable tool for clinical assessments. However, the complexity and variability of vestibular diseases pose challenges for conventional VNG methods, such as caloric, kinetic, and saccadic tests, in accurately identifying vertigo subtypes. Traditional diagnostic approaches often fail to fully utilize nystagmus characteristics in correlating with specific vestibular disorders, limiting their effectiveness. Recent advancements in artificial intelligence (AI), particularly deep learning and machine learning (ML), offer promising solutions for improving vertigo diagnosis. These technologies facilitate automated, rapid, and precise analysis by extracting relevant clinical features and classifying vestibular disorders with higher accuracy. ML-based models enhance diagnostic reliability, reducing human bias and subjectivity in assessment. This study reviews the latest research on feature extraction and ML applications in vertigo diagnosis, emphasizing their potential to revolutionize clinical decision-making. It aims to provide a comprehensive understanding of AI-driven approaches and their role in advancing vertigo analysis, paving the way for more effective diagnostic methodologies in the future.]]>
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