aceWavelet Scalogram-Based Nystagmus Classification for Automated Benign Paroxysmal Positional Vertigo Subtype Diagnosis
Benign paroxysmal positional vertigo (BPPV) is the most common cause of vestibular vertigo, yet accurate diagnosis remains challenging without specialist expertise. This study aimed to develop and evaluate an artificial intelligence (AI)-based diagnostic algorithm for BPPV using video-nystagmography (VNG) recordings. A total of 1352 VNG recordings were collected from patients with posterior semicircular canal (PSCC) BPPV (n = 234), lateral semicircular canal (LSCC) BPPV (canalolithiasis, n = 188; cupulolithiasis…
An AI model converting eye-tracking signals into wavelet scalograms reached recording-level accuracy of about 0.94 for posterior and lateral canal BPPV, suggesting automated diagnosis without specialist expertise is feasible.
For lateral canal BPPV, subtype F1-scores ranged from 0.59 to 0.98, with cupulolithiasis variants classified less well than canalolithiasis variants.
All data came from a single tertiary referral hospital, and the authors describe the results only as suggesting feasibility. Lateral canal subtype performance was uneven, especially for cupulolithiasis.
Evidence
- Peer-reviewedJournal of Imaging Informatics in Medicine2026-10-19
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Truvace Impact Record TRV-2026-2076, v1: “Wavelet Scalogram-Based Nystagmus Classification for Automated Benign Paroxysmal Positional Vertigo Subtype Diagnosis.” Truvace, 2026-10-21. /record/TRV-2026-2076 (accessed at citation time). sha256 5bdfdcb3ba314fcc…
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