Classification Prediction of Alzheimer’s Disease and Vascular Dementia Using Physiological Data and ECD SPECT Images
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
Affiliations
Yu-Ching Ni
Department of Radiation Protection, National Atomic Research Institute, Taoyuan 325, Taiwan
Zhi-Kun Lin
Department of Radiation Protection, National Atomic Research Institute, Taoyuan 325, Taiwan
Chen-Han Cheng
Department of Radiation Protection, National Atomic Research Institute, Taoyuan 325, Taiwan
Ming-Chyi Pai
Division of Behavioral Neurology, Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
Pai-Yi Chiu
Department of Neurology, Show Chwan Memorial Hospital, Changhua 500, Taiwan
Chiung-Chih Chang
Department of Neurology, Institute of Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833, Taiwan
Ya-Ting Chang
Department of Neurology, Institute of Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833, Taiwan
Guang-Uei Hung
Department of Nuclear Medicine, Chang Bing Show Chwan Memorial Hospital, Changhua 505, Taiwan
Kun-Ju Lin
Healthy Aging Research Center and Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan
Ing-Tsung Hsiao
Healthy Aging Research Center and Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan
Chia-Yu Lin
Department of Radiation Protection, National Atomic Research Institute, Taoyuan 325, Taiwan
Hui-Chieh Yang
Department of Radiation Protection, National Atomic Research Institute, Taoyuan 325, Taiwan
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.
