Diagnosis of Benign Positional Vertigo: A Systematic Review of Machine Learning and Deep Learning within Videonystagmography

Benign Positional Vertigo (BPV) is a common and correctable cause of dizziness worldwide, accompanied by unique nystagmus characteristics that can be recognized by trained healthcare workers. Nystagmus is an involuntary eye movement, consisting of an initial slow phase often followed by a subsequent quick phase, and is a key indicator of vestibular disorders including BPV. This review focuses on the application of machine learning models for BPV diagnosis through the classification of nystagmus patterns. We examine the advancements in machine learning and deep learning techniques for nystagmus detection, highlighting the transition from traditional methods to more sophisticated approaches. We include a comprehensive analysis of recent studies, detailing the methodologies, datasets, and results of various models. We discuss the ongoing challenges and future directions in this domain, emphasizing the potential of these technologies to assist diagnosis of BPV by untrained clinicians and the promise of better patient outcomes. Through a systematic literature review process, this article identifies gaps in current research and suggests areas for future exploration, aiming to support the application of artificial intelligence in the diagnosis of a common vertigo subtype.