ArticleSensors (Basel, Switzerland)2023
Vertical Nystagmus Recognition Based on Deep Learning.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Horizontal nystagmus identification with joint SAM segmentation and time series classification.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Article
- Artificial intelligence-driven clinical auxiliary diagnosis of benign paroxysmal positional vertigo.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Vertical nystagmus is a common neuro-ophthalmic sign in vestibular medicine. Vertical nystagmus not only reflects the functional state of vertical semicircular canal but also reflects the effect of otoliths. Medical experts can take nystagmus symptoms as the key factor to determine the cause of dizziness. Traditional observation (visual observation conducted by medical experts) may be biased subjectively. Visual examination also requires medical experts to have enough experience to make an accurate diagnosis. With the development of science and technology, the detection system for nystagmus can be realized by using artificial intelligence technology. In this paper, a vertical nystagmus recognition method is proposed based on deep learning. This method is mainly composed of a dilated convolution layer module, a depthwise separable convolution module, a convolution attention module, a Bilstm-GRU module, etc. The average recognition accuracy of the proposed method is 91%. Using the same training dataset and test set, the recognition accuracy of this method for vertical nystagmus was 2% higher than other methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.