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Parkinson's severity diagnosis explainable model based on 3D multi-head attention residual network.
Huang, Jiehui; Lin, Lishan; Yu, Fengcheng; He, Xuedong; Song, Wenhui; Lin, Jiaying; Tang, Zhenchao; Yuan, Kang; Li, Yucheng; Huang, Haofan; Pei, Zhong; Xian, Wenbiao; Yu-Chian Chen, Calvin.
Afiliación
  • Huang J; Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
  • Lin L; Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, 510080, China.
  • Yu F; Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
  • He X; School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China.
  • Song W; Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
  • Lin J; Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
  • Tang Z; Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
  • Yuan K; Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, 510080, China.
  • Li Y; Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, 510080, China.
  • Huang H; Polytechnic Institute, Zhejiang University, Hangzhou 310058, China.
  • Pei Z; Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, 510080, China. Electronic address: peizhong@mail.sysu.edu.c
  • Xian W; Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, 510080, China. Electronic address: xianwb@mail.sysu.edu.cn.
  • Yu-Chian Chen C; Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China; AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, Shenzhen, 518055, Guangdong, China; School of
Comput Biol Med ; 170: 107959, 2024 Mar.
Article en En | MEDLINE | ID: mdl-38215619
ABSTRACT
The severity evaluation of Parkinson's disease (PD) is of great significance for the treatment of PD. However, existing methods either have limitations based on prior knowledge or are invasive methods. To propose a more generalized severity evaluation model, this paper proposes an explainable 3D multi-head attention residual convolution network. First, we introduce the 3D attention-based convolution layer to extract video features. Second, features will be fed into LSTM and residual backbone networks, which can be used to capture the contextual information of the video. Finally, we design a feature compression module to condense the learned contextual features. We develop some interpretable experiments to better explain this black-box model so that it can be better generalized. Experiments show that our model can achieve state-of-the-art diagnosis performance. The proposed lightweight but effective model is expected to serve as a suitable end-to-end deep learning baseline in future research on PD video-based severity evaluation and has the potential for large-scale application in PD telemedicine. The source code is available at https//github.com/JackAILab/MARNet.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Telemedicina / Compresión de Datos Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
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PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Telemedicina / Compresión de Datos Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos