Construction and value of a vestibular function calibration test recognition model based on dual-stream ViT and ConvNeXt architecture
ObjectiveTo improve the efficiency and accuracy of videonystagmography calibration test results while enabling effective recognition of saccadic undershoot waveform by developing a dual-stream architecture-based deep learning model. MethodsA vestibular function calibration test recognition model wit...
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| Format: | Article |
| Language: | English |
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Shanghai Chinese Clinical Medicine Press Co., Ltd.
2025-04-01
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| Series: | Zhongguo Linchuang Yixue |
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| Online Access: | https://www.c-jcm.com/article/doi/10.12025/j.issn.1008-6358.2025.20250219 |
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| _version_ | 1850176455494860800 |
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| author | Xu LUO Peixia WU Weiming HAO Yinhong QU Han CHEN |
| author_facet | Xu LUO Peixia WU Weiming HAO Yinhong QU Han CHEN |
| author_sort | Xu LUO |
| collection | DOAJ |
| description | ObjectiveTo improve the efficiency and accuracy of videonystagmography calibration test results while enabling effective recognition of saccadic undershoot waveform by developing a dual-stream architecture-based deep learning model. MethodsA vestibular function calibration test recognition model with cross-modal feature fusion was constructed by integrating vision transformer (ViT) and a modified ConvNeXt convolutional network. The model utilized trajectory pictures and spatial distribution maps as inputs, employed a multi-task learning framework to classify calibration data, and to directly evaluate undershoot waveform. ResultsThe model showed outstanding performance in assessing calibration compliance. The accuracy, sensitivity, specificity of the model in left side, middle, and right side were all greater than 90%, and AUC values were all greater than 0.99, with 97.66% of optimal accuracy (middle), 98.98% of optimal sensitivity (middle), 96.87% of optimal specificity (right side), and 0.997 of AUC (right side). The model also showed promising performance in undershoot waveform recognition with 87.50% of accuracy, 89.66% of sensitivity, 85.71% of specificity, 86.67% of F1 score, and 0.931 of AUC. ConclusionsThe proposed method not only significantly enhances the efficiency and accuracy of calibration test results, but also provides a novel solution for undershoot waveform recognition. |
| format | Article |
| id | doaj-art-52cffd07695549d29f47535cf36b5a80 |
| institution | OA Journals |
| issn | 1008-6358 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Shanghai Chinese Clinical Medicine Press Co., Ltd. |
| record_format | Article |
| series | Zhongguo Linchuang Yixue |
| spelling | doaj-art-52cffd07695549d29f47535cf36b5a802025-08-20T02:19:15ZengShanghai Chinese Clinical Medicine Press Co., Ltd.Zhongguo Linchuang Yixue1008-63582025-04-0132220721110.12025/j.issn.1008-6358.2025.2025021920250219Construction and value of a vestibular function calibration test recognition model based on dual-stream ViT and ConvNeXt architectureXu LUO0Peixia WU1Weiming HAO2Yinhong QU3Han CHEN4Shanghai ZEHNIT Medical Technology Co., Ltd., Shanghai 201318, ChinaVertigo and Balance Function Disorders Clinical Center, EYE & ENT Hospital of Fudan University, Shanghai 200031, ChinaVertigo and Balance Function Disorders Clinical Center, EYE & ENT Hospital of Fudan University, Shanghai 200031, ChinaShanghai ZEHNIT Medical Technology Co., Ltd., Shanghai 201318, ChinaShanghai ZEHNIT Medical Technology Co., Ltd., Shanghai 201318, ChinaObjectiveTo improve the efficiency and accuracy of videonystagmography calibration test results while enabling effective recognition of saccadic undershoot waveform by developing a dual-stream architecture-based deep learning model. MethodsA vestibular function calibration test recognition model with cross-modal feature fusion was constructed by integrating vision transformer (ViT) and a modified ConvNeXt convolutional network. The model utilized trajectory pictures and spatial distribution maps as inputs, employed a multi-task learning framework to classify calibration data, and to directly evaluate undershoot waveform. ResultsThe model showed outstanding performance in assessing calibration compliance. The accuracy, sensitivity, specificity of the model in left side, middle, and right side were all greater than 90%, and AUC values were all greater than 0.99, with 97.66% of optimal accuracy (middle), 98.98% of optimal sensitivity (middle), 96.87% of optimal specificity (right side), and 0.997 of AUC (right side). The model also showed promising performance in undershoot waveform recognition with 87.50% of accuracy, 89.66% of sensitivity, 85.71% of specificity, 86.67% of F1 score, and 0.931 of AUC. ConclusionsThe proposed method not only significantly enhances the efficiency and accuracy of calibration test results, but also provides a novel solution for undershoot waveform recognition.https://www.c-jcm.com/article/doi/10.12025/j.issn.1008-6358.2025.20250219vestibular functioncalibration testvideonystagmographydeep learning model |
| spellingShingle | Xu LUO Peixia WU Weiming HAO Yinhong QU Han CHEN Construction and value of a vestibular function calibration test recognition model based on dual-stream ViT and ConvNeXt architecture Zhongguo Linchuang Yixue vestibular function calibration test videonystagmography deep learning model |
| title | Construction and value of a vestibular function calibration test recognition model based on dual-stream ViT and ConvNeXt architecture |
| title_full | Construction and value of a vestibular function calibration test recognition model based on dual-stream ViT and ConvNeXt architecture |
| title_fullStr | Construction and value of a vestibular function calibration test recognition model based on dual-stream ViT and ConvNeXt architecture |
| title_full_unstemmed | Construction and value of a vestibular function calibration test recognition model based on dual-stream ViT and ConvNeXt architecture |
| title_short | Construction and value of a vestibular function calibration test recognition model based on dual-stream ViT and ConvNeXt architecture |
| title_sort | construction and value of a vestibular function calibration test recognition model based on dual stream vit and convnext architecture |
| topic | vestibular function calibration test videonystagmography deep learning model |
| url | https://www.c-jcm.com/article/doi/10.12025/j.issn.1008-6358.2025.20250219 |
| work_keys_str_mv | AT xuluo constructionandvalueofavestibularfunctioncalibrationtestrecognitionmodelbasedondualstreamvitandconvnextarchitecture AT peixiawu constructionandvalueofavestibularfunctioncalibrationtestrecognitionmodelbasedondualstreamvitandconvnextarchitecture AT weiminghao constructionandvalueofavestibularfunctioncalibrationtestrecognitionmodelbasedondualstreamvitandconvnextarchitecture AT yinhongqu constructionandvalueofavestibularfunctioncalibrationtestrecognitionmodelbasedondualstreamvitandconvnextarchitecture AT hanchen constructionandvalueofavestibularfunctioncalibrationtestrecognitionmodelbasedondualstreamvitandconvnextarchitecture |