ArticleFrontiers in neuroscience2023
Torsional nystagmus recognition based on deep learning for vertigo diagnosis.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Article
- Transformer-Based Detection and Clinical Evaluation System for Torsional Nystagmus.Sensors (Basel, Switzerland) · 2025Article
- Article
- Improving the Diagnostic Workup of Vertigo: A Multidisciplinary Review.Spartan medical research journal · 2025Review
- Transforming ENT Healthcare: Advancements and Implications of Artificial Intelligence.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2024Article
- Deep Learning-Based Nystagmus Detection for BPPV Diagnosis.Sensors (Basel, Switzerland) · 2024Article
- Artificial intelligence-driven clinical auxiliary diagnosis of benign paroxysmal positional vertigo.Frontiers in neurologyReview
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Detection of torsional nystagmus can help identify the canal of origin in benign paroxysmal positional vertigo (BPPV). Most currently available pupil trackers do not detect torsional nystagmus. In view of this, a new deep learning network model was designed for the determination of torsional nystagmus. Methods: The data set comes from the Eye, Ear, Nose and Throat (Eye&ENT) Hospital of Fudan University. In the process of data acquisition, the infrared videos were obtained from eye movement recorder. The dataset contains 24521 nystagmus videos. All torsion nystagmus videos were annotated by the ophthalmologist of the hospital. 80% of the data set was used to train the model, and 20% was used to test. Results: Experiments indicate that the designed method can effectively identify torsional nystagmus. Compared with other methods, it has high recognition accuracy. It can realize the automatic recognition of torsional nystagmus and provides support for the posterior and anterior canal BPPV diagnosis. Discussion: Our present work complements existing methods of 2D nystagmus analysis and could improve the diagnostic capabilities of VNG in multiple vestibular disorders. To automatically pick BPV requires detection of nystagmus in all 3 planes and identification of a paroxysm. This is the next research work to be carried out.
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