RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application Article Swipe
Wen Lu
,
Zhuangzhuang Li
,
Yini Li
,
Jie Li
,
Zhengnong Chen
,
Yanmei Feng
,
Hui Wang
,
Qiong Luo
,
Yiqing Wang
,
Jun Pan
,
Lingyun Gu
,
Dongzhen Yu
,
Yudong Zhang
,
Haibo Shi
,
Shankai Yin
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2022.930028
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2022.930028
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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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnins.2022.930028
- https://www.frontiersin.org/articles/10.3389/fnins.2022.930028/pdf
- OA Status
- gold
- Cited By
- 10
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4282583049
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4282583049Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fnins.2022.930028Digital Object Identifier
- Title
-
RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary ApplicationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-13Full publication date if available
- Authors
-
Wen Lu, Zhuangzhuang Li, Yini Li, Jie Li, Zhengnong Chen, Yanmei Feng, Hui Wang, Qiong Luo, Yiqing Wang, Jun Pan, Lingyun Gu, Dongzhen Yu, Yudong Zhang, Haibo Shi, Shankai YinList of authors in order
- Landing page
-
https://doi.org/10.3389/fnins.2022.930028Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fnins.2022.930028/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/articles/10.3389/fnins.2022.930028/pdfDirect OA link when available
- Concepts
-
Nystagmus, Benign paroxysmal positional vertigo, Vertigo, Convolutional neural network, Artificial intelligence, Computer science, Eye movement, Deep learning, Medicine, Audiology, SurgeryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 1, 2023: 6Per-year citation counts (last 5 years)
- References (count)
-
50Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.automatic | 30, 96 |
| abstract_inverted_index.detection | 46, 73, 132 |
| abstract_inverted_index.diagnosis | 31, 101 |
| abstract_inverted_index.nystagmus | 21, 66, 72, 131 |
| abstract_inverted_index.torsional | 92 |
| abstract_inverted_index.diagnosis, | 134 |
| abstract_inverted_index.frequently | 4 |
| abstract_inverted_index.horizontal | 83 |
| abstract_inverted_index.nystagmus. | 13 |
| abstract_inverted_index.paroxysmal | 27, 98 |
| abstract_inverted_index.positional | 28, 99 |
| abstract_inverted_index.practices. | 143 |
| abstract_inverted_index.real-world | 141 |
| abstract_inverted_index.reference, | 129 |
| abstract_inverted_index.accompanied | 9 |
| abstract_inverted_index.constructed | 49 |
| abstract_inverted_index.dimensions. | 70 |
| abstract_inverted_index.facilitates | 130 |
| abstract_inverted_index.recognition | 22 |
| abstract_inverted_index.sensitivity | 105 |
| abstract_inverted_index.specificity | 108 |
| abstract_inverted_index.Convolutional | 58 |
| abstract_inverted_index.architectures | 38 |
| abstract_inverted_index.eye-movements | 11 |
| abstract_inverted_index.network-based | 60 |
| abstract_inverted_index.three-dimensional | 20 |
| abstract_inverted_index.ChiCTR-IOR-17010506). | 43 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5017155898, https://openalex.org/A5100434437, https://openalex.org/A5039541195 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 15 |
| corresponding_institution_ids | https://openalex.org/I153648349, https://openalex.org/I183067930, https://openalex.org/I4210144482 |
| citation_normalized_percentile.value | 0.73553126 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |