Classification Prediction of Alzheimer’s Disease and Vascular Dementia Using Physiological Data and ECD SPECT Images
Alzheimer’s disease (AD) and vascular dementia (VaD) are the two most common forms of dementia. However, their neuropsychological and pathological features often overlap, making it difficult to distinguish between AD and VaD. In addition to clinical consultation and laboratory examinations, clinical...
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| Format: | Article |
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MDPI AG
2024-02-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/4/365 |
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| author | Yu-Ching Ni Zhi-Kun Lin Chen-Han Cheng Ming-Chyi Pai Pai-Yi Chiu Chiung-Chih Chang Ya-Ting Chang Guang-Uei Hung Kun-Ju Lin Ing-Tsung Hsiao Chia-Yu Lin Hui-Chieh Yang |
| author_facet | Yu-Ching Ni Zhi-Kun Lin Chen-Han Cheng Ming-Chyi Pai Pai-Yi Chiu Chiung-Chih Chang Ya-Ting Chang Guang-Uei Hung Kun-Ju Lin Ing-Tsung Hsiao Chia-Yu Lin Hui-Chieh Yang |
| author_sort | Yu-Ching Ni |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) and vascular dementia (VaD) are the two most common forms of dementia. However, their neuropsychological and pathological features often overlap, making it difficult to distinguish between AD and VaD. In addition to clinical consultation and laboratory examinations, clinical dementia diagnosis in Taiwan will also include Tc-99m-ECD SPECT imaging examination. Through machine learning and deep learning technology, we explored the feasibility of using the above clinical practice data to distinguish AD and VaD. We used the physiological data (33 features) and Tc-99m-ECD SPECT images of 112 AD patients and 85 VaD patients in the Taiwanese Nuclear Medicine Brain Image Database to train the classification model. The results, after filtering by the number of SVM RFE 5-fold features, show that the average accuracy of physiological data in distinguishing AD/VaD is 81.22% and the AUC is 0.836; the average accuracy of training images using the Inception V3 model is 85% and the AUC is 0.95. Finally, Grad-CAM heatmap was used to visualize the areas of concern of the model and compared with the SPM analysis method to further understand the differences. This research method can quickly use machine learning and deep learning models to automatically extract image features based on a small amount of general clinical data to objectively distinguish AD and VaD. |
| first_indexed | 2024-03-07T22:35:14Z |
| format | Article |
| id | doaj.art-1d3bd36bcfdf46169e02ce387203a5ae |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-07T22:35:14Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-1d3bd36bcfdf46169e02ce387203a5ae2024-02-23T15:13:41ZengMDPI AGDiagnostics2075-44182024-02-0114436510.3390/diagnostics14040365Classification Prediction of Alzheimer’s Disease and Vascular Dementia Using Physiological Data and ECD SPECT ImagesYu-Ching Ni0Zhi-Kun Lin1Chen-Han Cheng2Ming-Chyi Pai3Pai-Yi Chiu4Chiung-Chih Chang5Ya-Ting Chang6Guang-Uei Hung7Kun-Ju Lin8Ing-Tsung Hsiao9Chia-Yu Lin10Hui-Chieh Yang11Department of Radiation Protection, National Atomic Research Institute, Taoyuan 325, TaiwanDepartment of Radiation Protection, National Atomic Research Institute, Taoyuan 325, TaiwanDepartment of Radiation Protection, National Atomic Research Institute, Taoyuan 325, TaiwanDivision of Behavioral Neurology, Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 701, TaiwanDepartment of Neurology, Show Chwan Memorial Hospital, Changhua 500, TaiwanDepartment of Neurology, Institute of Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833, TaiwanDepartment of Neurology, Institute of Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833, TaiwanDepartment of Nuclear Medicine, Chang Bing Show Chwan Memorial Hospital, Changhua 505, TaiwanHealthy Aging Research Center and Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333, TaiwanHealthy Aging Research Center and Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333, TaiwanDepartment of Radiation Protection, National Atomic Research Institute, Taoyuan 325, TaiwanDepartment of Radiation Protection, National Atomic Research Institute, Taoyuan 325, TaiwanAlzheimer’s disease (AD) and vascular dementia (VaD) are the two most common forms of dementia. However, their neuropsychological and pathological features often overlap, making it difficult to distinguish between AD and VaD. In addition to clinical consultation and laboratory examinations, clinical dementia diagnosis in Taiwan will also include Tc-99m-ECD SPECT imaging examination. Through machine learning and deep learning technology, we explored the feasibility of using the above clinical practice data to distinguish AD and VaD. We used the physiological data (33 features) and Tc-99m-ECD SPECT images of 112 AD patients and 85 VaD patients in the Taiwanese Nuclear Medicine Brain Image Database to train the classification model. The results, after filtering by the number of SVM RFE 5-fold features, show that the average accuracy of physiological data in distinguishing AD/VaD is 81.22% and the AUC is 0.836; the average accuracy of training images using the Inception V3 model is 85% and the AUC is 0.95. Finally, Grad-CAM heatmap was used to visualize the areas of concern of the model and compared with the SPM analysis method to further understand the differences. This research method can quickly use machine learning and deep learning models to automatically extract image features based on a small amount of general clinical data to objectively distinguish AD and VaD.https://www.mdpi.com/2075-4418/14/4/365ECD SPECT imagesAlzheimer’s diseasevascular dementiaclassification prediction |
| spellingShingle | Yu-Ching Ni Zhi-Kun Lin Chen-Han Cheng Ming-Chyi Pai Pai-Yi Chiu Chiung-Chih Chang Ya-Ting Chang Guang-Uei Hung Kun-Ju Lin Ing-Tsung Hsiao Chia-Yu Lin Hui-Chieh Yang Classification Prediction of Alzheimer’s Disease and Vascular Dementia Using Physiological Data and ECD SPECT Images Diagnostics ECD SPECT images Alzheimer’s disease vascular dementia classification prediction |
| title | Classification Prediction of Alzheimer’s Disease and Vascular Dementia Using Physiological Data and ECD SPECT Images |
| title_full | Classification Prediction of Alzheimer’s Disease and Vascular Dementia Using Physiological Data and ECD SPECT Images |
| title_fullStr | Classification Prediction of Alzheimer’s Disease and Vascular Dementia Using Physiological Data and ECD SPECT Images |
| title_full_unstemmed | Classification Prediction of Alzheimer’s Disease and Vascular Dementia Using Physiological Data and ECD SPECT Images |
| title_short | Classification Prediction of Alzheimer’s Disease and Vascular Dementia Using Physiological Data and ECD SPECT Images |
| title_sort | classification prediction of alzheimer s disease and vascular dementia using physiological data and ecd spect images |
| topic | ECD SPECT images Alzheimer’s disease vascular dementia classification prediction |
| url | https://www.mdpi.com/2075-4418/14/4/365 |
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