Development of a cerebellar ataxia diagnosis model using conditional GAN-based synthetic data generation for visuomotor adaptation task
Abstract This study proposes a synthetic data generation model to create a classification framework for cerebellar ataxia patients using trajectory data from the visuomotor adaptation task. The classification objectives include patients with cerebellar ataxia, age-matched normal individuals, and you...
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| Format: | Article |
| Language: | English |
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BMC
2024-11-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02720-y |
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| author | Jinah Kim Sung-Ho Woo Taekyung Kim Won Tae Yoon Jung Hwan Shin Jee-Young Lee Jeh-Kwang Ryu |
| author_facet | Jinah Kim Sung-Ho Woo Taekyung Kim Won Tae Yoon Jung Hwan Shin Jee-Young Lee Jeh-Kwang Ryu |
| author_sort | Jinah Kim |
| collection | DOAJ |
| description | Abstract This study proposes a synthetic data generation model to create a classification framework for cerebellar ataxia patients using trajectory data from the visuomotor adaptation task. The classification objectives include patients with cerebellar ataxia, age-matched normal individuals, and young healthy subjects. Synthetic data for the three classes is generated based on class conditions and random noise by leveraging a combination of conditional adversarial generative neural networks and reconstruction networks. This synthetic data, alongside real data, is utilized as training data for the patient classification model to enhance classification accuracy. The fidelity of the synthetic data is assessed visually to measure the validity and diversity of the generated data qualitatively while quantitatively evaluating distribution similarity to real data. Furthermore, the clinical efficacy of the patient classification model employing synthetic data is demonstrated by showcasing improved classification accuracy through a comparative analysis between results obtained using solely real data and those obtained when both real and synthetic data are utilized. This methodological approach holds promise in addressing data insufficiency in the digital healthcare domain, employing deep learning methodologies, and developing early disease diagnosis tools. |
| format | Article |
| id | doaj-art-d0f7152faac7434cad983033a7f457e9 |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-d0f7152faac7434cad983033a7f457e92024-11-17T12:31:44ZengBMCBMC Medical Informatics and Decision Making1472-69472024-11-0124111510.1186/s12911-024-02720-yDevelopment of a cerebellar ataxia diagnosis model using conditional GAN-based synthetic data generation for visuomotor adaptation taskJinah Kim0Sung-Ho Woo1Taekyung Kim2Won Tae Yoon3Jung Hwan Shin4Jee-Young Lee5Jeh-Kwang Ryu6Coastal Disaster Research Center, Korea Institute of Ocean Science and TechnologyInstitute of Interdisciplinary Brain Science, Dongguk University College of MedicineCoastal Disaster Research Center, Korea Institute of Ocean Science and TechnologyDepartment of Neurology, Samsung Kangbuk Hospital, Sungkyunkwan University School of MedicineDepartment of Neurology, Seoul National University Hospital, Seoul National University College of MedicineDepartment of Neurology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul National University College of MedicineLaboratory for Natural and Artificial Kinästhese, Convergence Research Center for Artificial Intelligence, Dongguk UniversityAbstract This study proposes a synthetic data generation model to create a classification framework for cerebellar ataxia patients using trajectory data from the visuomotor adaptation task. The classification objectives include patients with cerebellar ataxia, age-matched normal individuals, and young healthy subjects. Synthetic data for the three classes is generated based on class conditions and random noise by leveraging a combination of conditional adversarial generative neural networks and reconstruction networks. This synthetic data, alongside real data, is utilized as training data for the patient classification model to enhance classification accuracy. The fidelity of the synthetic data is assessed visually to measure the validity and diversity of the generated data qualitatively while quantitatively evaluating distribution similarity to real data. Furthermore, the clinical efficacy of the patient classification model employing synthetic data is demonstrated by showcasing improved classification accuracy through a comparative analysis between results obtained using solely real data and those obtained when both real and synthetic data are utilized. This methodological approach holds promise in addressing data insufficiency in the digital healthcare domain, employing deep learning methodologies, and developing early disease diagnosis tools.https://doi.org/10.1186/s12911-024-02720-yCerebellar ataxia diagnosisVisuomotor adaptation taskConditional generative adversarial networkSynthetic dataDigital healthcare |
| spellingShingle | Jinah Kim Sung-Ho Woo Taekyung Kim Won Tae Yoon Jung Hwan Shin Jee-Young Lee Jeh-Kwang Ryu Development of a cerebellar ataxia diagnosis model using conditional GAN-based synthetic data generation for visuomotor adaptation task BMC Medical Informatics and Decision Making Cerebellar ataxia diagnosis Visuomotor adaptation task Conditional generative adversarial network Synthetic data Digital healthcare |
| title | Development of a cerebellar ataxia diagnosis model using conditional GAN-based synthetic data generation for visuomotor adaptation task |
| title_full | Development of a cerebellar ataxia diagnosis model using conditional GAN-based synthetic data generation for visuomotor adaptation task |
| title_fullStr | Development of a cerebellar ataxia diagnosis model using conditional GAN-based synthetic data generation for visuomotor adaptation task |
| title_full_unstemmed | Development of a cerebellar ataxia diagnosis model using conditional GAN-based synthetic data generation for visuomotor adaptation task |
| title_short | Development of a cerebellar ataxia diagnosis model using conditional GAN-based synthetic data generation for visuomotor adaptation task |
| title_sort | development of a cerebellar ataxia diagnosis model using conditional gan based synthetic data generation for visuomotor adaptation task |
| topic | Cerebellar ataxia diagnosis Visuomotor adaptation task Conditional generative adversarial network Synthetic data Digital healthcare |
| url | https://doi.org/10.1186/s12911-024-02720-y |
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