國家衛生研究院 NHRI:Item 3990099045/16720
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    题名: Learning-based progression detection of Alzheimer's disease using 3D MRI images
    作者: Wu, JCH;Chien, TC;Chang, CC;Chang, H;Tsai, HJ;Lan, MY;Wu, NC;Lu, HHS
    贡献者: Institute of Population Health Sciences
    摘要: Alzheimer's disease (AD) is an irreversible brain disease. In addition to the functional deterioration of memory and cognition, patients with severe conditions lose their self-care ability. Patients exhibiting symptoms are often attributed to aging and thus lack proper medical care. If it can be diagnosed early, the doctor can provide adequate treatments to mitigate the symptoms. Magnetic resonance imaging (MRI) can reflect the characteristics of different human tissues and organs, and is a common tool implemented in clinical examinations. In this study, we tested learning-based approaches to detect disease progression in AD patients using MRI. Specifically, each patient is categorized as one of the following four classes: cognitive normal, early mild cognitive impairment, late mild cognitive impairment, and AD. To extract 3D information in MRI, we proposed a 3D convolutional neural network structure based on ResNet3D-18. We designed various multiclass classification frameworks. Moreover, we implemented ensemble classification combining these frameworks. Experiments demonstrated great potential for learning-based approaches on the Alzheimer's Disease Neuroimaging Initiative dataset. The ensemble approach performed the best with an accuracy of 0.950, which is competitive with neurologists in diagnosing AD progression in clinical practice. With precise detection, patients can understand their conditions early and seek proper treatments.
    日期: 2025-01-15
    關聯: International Journal of Intelligent Systems. 2025 Jan 15;Article in Press.
    Link to: http://dx.doi.org/10.1155/int/3981977
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=0884-8173&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:001395958600001
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