干旱区生态环境知识资源中心(Arid): Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach

Arid

DOI	10.3390/biomedicines12040896
	Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach
	Cheung, Eva Y. W.; Wu, Ricky W. K.; Chu, Ellie S. M.; Mak, Henry K. F.
通讯作者	Cheung, EYW
来源期刊	BIOMEDICINES
EISSN	2227-9059
出版年	2024
卷号	12 期号:4
英文摘要	Background: MRI magnetization-prepared rapid acquisition (MPRAGE) is an easily available imaging modality for dementia diagnosis. Previous studies suggested that volumetric analysis plays a crucial role in various stages of dementia classification. In this study, volumetry, radiomics and demographics were integrated as inputs to develop an artificial intelligence model for various stages, including Alzheimer's disease (AD), mild cognitive decline (MCI) and cognitive normal (CN) dementia classifications. Method: The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset was separated into training and testing groups, and the Open Access Series of Imaging Studies (OASIS) dataset was used as the second testing group. The MRI MPRAGE image was reoriented via statistical parametric mapping (SPM12). Freesurfer was employed for brain segmentation, and 45 regional brain volumes were retrieved. The 3D Slicer software was employed for 107 radiomics feature extractions from within the whole brain. Data on patient demographics were collected from the datasets. The feed-forward neural network (FFNN) and the other most common artificial intelligence algorithms, including support vector machine (SVM), ensemble classifier (EC) and decision tree (DT), were used to build the models using various features. Results: The integration of brain regional volumes, radiomics and patient demographics attained the highest overall accuracy at 76.57% and 73.14% in ADNI and OASIS testing, respectively. The subclass accuracies in MCI, AD and CN were 78.29%, 89.71% and 85.14%, respectively, in ADNI testing, as well as 74.86%, 88% and 83.43% in OASIS testing. Balanced sensitivity and specificity were obtained for all subclass classifications in MCI, AD and CN. Conclusion: The FFNN yielded good overall accuracy for MCI, AD and CN categorization, with balanced subclass accuracy, sensitivity and specificity. The proposed FFNN model is simple, and it may support the triage of patients for further confirmation of the diagnosis.
英文关键词	Alzheimer's disease mild cognitive impairment dementia radiomics volumetry feed forward neural network artificial neural network
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:001210604100001
WOS关键词	SURFACE-BASED ANALYSIS ; TEMPORAL-LOBE ATROPHY ; ALZHEIMERS-DISEASE ; STRUCTURAL MRI ; DIFFERENTIAL-DIAGNOSIS ; SEGMENTATION ; PREDICTION ; ACCURATE
WOS类目	Biochemistry & Molecular Biology ; Medicine, Research & Experimental ; Pharmacology & Pharmacy
WOS研究方向	Biochemistry & Molecular Biology ; Research & Experimental Medicine ; Pharmacology & Pharmacy
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403035
推荐引用方式 GB/T 7714	Cheung, Eva Y. W.,Wu, Ricky W. K.,Chu, Ellie S. M.,et al. Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach[J],2024,12(4).
APA	Cheung, Eva Y. W.,Wu, Ricky W. K.,Chu, Ellie S. M.,&Mak, Henry K. F..(2024).Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach.BIOMEDICINES,12(4).
MLA	Cheung, Eva Y. W.,et al."Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach".BIOMEDICINES 12.4(2024).