An Integrated Multi-Channel Deep Neural Network for Mesial Temporal Lobe Epilepsy Identification Using Multi-Modal Medical Data
Epilepsy is a chronic brain disease with recurrent seizures. Mesial temporal lobe epilepsy (MTLE) is the most common pathological cause of epilepsy. With the development of computer-aided diagnosis technology, there are many auxiliary diagnostic approaches based on deep learning algorithms. However,...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/10/10/1234 |
| _version_ | 1797574682589790208 |
|---|---|
| author | Ruowei Qu Xuan Ji Shifeng Wang Zhaonan Wang Le Wang Xinsheng Yang Shaoya Yin Junhua Gu Alan Wang Guizhi Xu |
| author_facet | Ruowei Qu Xuan Ji Shifeng Wang Zhaonan Wang Le Wang Xinsheng Yang Shaoya Yin Junhua Gu Alan Wang Guizhi Xu |
| author_sort | Ruowei Qu |
| collection | DOAJ |
| description | Epilepsy is a chronic brain disease with recurrent seizures. Mesial temporal lobe epilepsy (MTLE) is the most common pathological cause of epilepsy. With the development of computer-aided diagnosis technology, there are many auxiliary diagnostic approaches based on deep learning algorithms. However, the causes of epilepsy are complex, and distinguishing different types of epilepsy accurately is challenging with a single mode of examination. In this study, our aim is to assess the combination of multi-modal epilepsy medical information from structural MRI, PET image, typical clinical symptoms and personal demographic and cognitive data (PDC) by adopting a multi-channel 3D deep convolutional neural network and pre-training PET images. The results show better diagnosis accuracy than using one single type of medical data alone. These findings reveal the potential of a deep neural network in multi-modal medical data fusion. |
| first_indexed | 2024-03-10T21:25:47Z |
| format | Article |
| id | doaj.art-b7ea0f1d91d04e67ba29e3362bb3e7e2 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-10T21:25:47Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-b7ea0f1d91d04e67ba29e3362bb3e7e22023-11-19T15:42:54ZengMDPI AGBioengineering2306-53542023-10-011010123410.3390/bioengineering10101234An Integrated Multi-Channel Deep Neural Network for Mesial Temporal Lobe Epilepsy Identification Using Multi-Modal Medical DataRuowei Qu0Xuan Ji1Shifeng Wang2Zhaonan Wang3Le Wang4Xinsheng Yang5Shaoya Yin6Junhua Gu7Alan Wang8Guizhi Xu9State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, ChinaState Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, ChinaTianjin Universal Medical Imaging Diagnostic Center, Tianjin 300110, ChinaState Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, ChinaDepartment of Functional Neurosurgery, Huanhu Hospital, Tianjin 300350, ChinaState Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, ChinaDepartment of Functional Neurosurgery, Huanhu Hospital, Tianjin 300350, ChinaState Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, ChinaAuckland Bioengineering Institute, The University of Auckland, Grafton, Auckland 1010, New ZealandState Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, ChinaEpilepsy is a chronic brain disease with recurrent seizures. Mesial temporal lobe epilepsy (MTLE) is the most common pathological cause of epilepsy. With the development of computer-aided diagnosis technology, there are many auxiliary diagnostic approaches based on deep learning algorithms. However, the causes of epilepsy are complex, and distinguishing different types of epilepsy accurately is challenging with a single mode of examination. In this study, our aim is to assess the combination of multi-modal epilepsy medical information from structural MRI, PET image, typical clinical symptoms and personal demographic and cognitive data (PDC) by adopting a multi-channel 3D deep convolutional neural network and pre-training PET images. The results show better diagnosis accuracy than using one single type of medical data alone. These findings reveal the potential of a deep neural network in multi-modal medical data fusion.https://www.mdpi.com/2306-5354/10/10/1234mesial temporal lobe epilepsymulti-modal data confusionneural networkdeep learningepilepsymedical image analysis |
| spellingShingle | Ruowei Qu Xuan Ji Shifeng Wang Zhaonan Wang Le Wang Xinsheng Yang Shaoya Yin Junhua Gu Alan Wang Guizhi Xu An Integrated Multi-Channel Deep Neural Network for Mesial Temporal Lobe Epilepsy Identification Using Multi-Modal Medical Data Bioengineering mesial temporal lobe epilepsy multi-modal data confusion neural network deep learning epilepsy medical image analysis |
| title | An Integrated Multi-Channel Deep Neural Network for Mesial Temporal Lobe Epilepsy Identification Using Multi-Modal Medical Data |
| title_full | An Integrated Multi-Channel Deep Neural Network for Mesial Temporal Lobe Epilepsy Identification Using Multi-Modal Medical Data |
| title_fullStr | An Integrated Multi-Channel Deep Neural Network for Mesial Temporal Lobe Epilepsy Identification Using Multi-Modal Medical Data |
| title_full_unstemmed | An Integrated Multi-Channel Deep Neural Network for Mesial Temporal Lobe Epilepsy Identification Using Multi-Modal Medical Data |
| title_short | An Integrated Multi-Channel Deep Neural Network for Mesial Temporal Lobe Epilepsy Identification Using Multi-Modal Medical Data |
| title_sort | integrated multi channel deep neural network for mesial temporal lobe epilepsy identification using multi modal medical data |
| topic | mesial temporal lobe epilepsy multi-modal data confusion neural network deep learning epilepsy medical image analysis |
| url | https://www.mdpi.com/2306-5354/10/10/1234 |
| work_keys_str_mv | AT ruoweiqu anintegratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT xuanji anintegratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT shifengwang anintegratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT zhaonanwang anintegratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT lewang anintegratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT xinshengyang anintegratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT shaoyayin anintegratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT junhuagu anintegratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT alanwang anintegratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT guizhixu anintegratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT ruoweiqu integratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT xuanji integratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT shifengwang integratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT zhaonanwang integratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT lewang integratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT xinshengyang integratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT shaoyayin integratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT junhuagu integratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT alanwang integratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata AT guizhixu integratedmultichanneldeepneuralnetworkformesialtemporallobeepilepsyidentificationusingmultimodalmedicaldata |