AI-enabled nystagmus analysis in vestibular assessment: Current evidence and future directions.
TL;DR
AI-enabled nystagmus analysis shows promising clinical potential, particularly for VNG/VOG-based assessment and BPPV-related decision support, but translation into routine practice will require multicentre patient-level external validation, transparent reporting, interpretable outputs, standardised performance metrics, and prospective evaluation in real clinical pathways.
Abstract
Vestibular disorders are common, heterogeneous, and diagnostically challenging. Nystagmus is an important sign in vestibular assessment, but its interpretation can depend on clinician expertise and recording quality. This structured narrative review mapped current evidence on artificial intelligence (AI)-enabled nystagmus and eye-movement signal analysis in vestibular assessment, focusing on study characteristics, input data, AI tasks, model architectures, validation approaches, performance metrics, explainability, and clinical applicability. A structured PubMed search identified human studies that developed, validated, or evaluated machine-learning, deep-learning, or related computational methods for nystagmus analysis or vestibular diagnostic support. Fifteen studies were included, most published from 2021 onwards. The evidence was dominated by technical and early-stage model-development studies using VNG/VOG recordings, other eye-movement recordings, or derived features such as pupil trajectories, optical-flow maps, eye and head traces, and slow-phase velocity measures. Applications included nystagmus detection, pupil tracking and signal extraction, pattern or direction classification, quantification or localisation, and benign paroxysmal positional vertigo-related diagnostic classification. Deep-learning approaches, particularly convolutional neural networks and CNN-recurrent hybrid models, were commonly used. However, validation was mainly internal, performance reporting was heterogeneous, explainability was limited, and prospective workflow evaluation or routine clinical implementation was uncommon. AI-enabled nystagmus analysis shows promising clinical potential, particularly for VNG/VOG-based assessment and BPPV-related decision support. Translation into routine practice will require multicentre patient-level external validation, transparent reporting, interpretable outputs, standardised performance metrics, and prospective evaluation in real clinical pathways.