RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application
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 (Ch...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.930028/full |
| _version_ | 1797243484762013696 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Wen Lu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T18:54:32Z |
| format | Article |
| id | doaj.art-0c543ab8b8f74da1a516dcc9d4d46307 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-24T18:55:51Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-0c543ab8b8f74da1a516dcc9d4d463072024-03-26T16:37:25ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-06-011610.3389/fnins.2022.930028930028RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary ApplicationWen Lu0Zhuangzhuang Li1Yini Li2Jie Li3Zhengnong Chen4Yanmei Feng5Hui Wang6Qiong Luo7Yiqing Wang8Jun Pan9Lingyun Gu10Dongzhen Yu11Yudong Zhang12Haibo Shi13Shankai Yin14Department of Otolaryngology—Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, ChinaDepartment of Otolaryngology—Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, ChinaDepartment of Otolaryngology—Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, ChinaDepartment of Otolaryngology—Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, ChinaDepartment of Otolaryngology—Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, ChinaDepartment of Otolaryngology—Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, ChinaDepartment of Otolaryngology—Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, ChinaDepartment of Otolaryngology—Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, ChinaIceKredit Inc., Shanghai, ChinaIceKredit Inc., Shanghai, ChinaIceKredit Inc., Shanghai, ChinaDepartment of Otolaryngology—Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, ChinaSchool of Computing and Mathematical Sciences, University of Leicester, Leicester, United KingdomDepartment of Otolaryngology—Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, ChinaDepartment of Otolaryngology—Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, ChinaSymptoms 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.https://www.frontiersin.org/articles/10.3389/fnins.2022.930028/fullvertigonystagmus detectionbenign paroxysmal positional vertigodeep learningneural network |
| spellingShingle | 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 RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application Frontiers in Neuroscience vertigo nystagmus detection benign paroxysmal positional vertigo deep learning neural network |
| title | RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application |
| title_full | RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application |
| title_fullStr | RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application |
| title_full_unstemmed | RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application |
| title_short | RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application |
| title_sort | retracted a deep learning model for three dimensional nystagmus detection and its preliminary application |
| topic | vertigo nystagmus detection benign paroxysmal positional vertigo deep learning neural network |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.930028/full |
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