ArticleFrontiers in neuroscience2022
A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 7 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 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
- Neurological update: neuro-otology 2023.Journal of neurology · 2023Article
- Diagnosing the benign paroxysmal positional vertigo via 1D and deep-learning composite model.Journal of neurology · 2023Article
- Vertical Nystagmus Recognition Based on Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Torsional nystagmus recognition based on deep learning for vertigo diagnosis.Frontiers in neuroscience · 2023Article
- 3D shape reconstruction with a multiple-constraint estimation approach.Frontiers in neuroscience · 2023Article
Corrections and comments
- Retraction · 2023-12-28Compromised Peer Review · Investigation by Journal/Publisher · Objections by Author(s) · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Symptoms of vertigo are frequently reported and are usually accompanied by eye-movements called nystagmus. In this article, we designed a three-dimensional nystagmus recognition model and a benign paroxysmal positional vertigo automatic diagnosis system based on deep neural network architectures (Chinese Clinical Trials Registry ChiCTR-IOR-17010506). An object detection model was constructed to track the movement of the pupil centre. Convolutional neural network-based models were trained to detect nystagmus patterns in three dimensions. Our nystagmus detection models obtained high areas under the curve; 0.982 in horizontal tests, 0.893 in vertical tests, and 0.957 in torsional tests. Moreover, our automatic benign paroxysmal positional vertigo diagnosis system achieved a sensitivity of 0.8848, specificity of 0.8841, accuracy of 0.8845, and an F1 score of 0.8914. Compared with previous studies, our system provides a clinical reference, facilitates nystagmus detection and diagnosis, and it can be applied in real-world medical practices.
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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.