Decoding degeneration:the implementation of machine learning for clinical detection of neurodegenerative disorders

Fariha Khaliq; Jane Oberhauser; Debia Wakhloo; Sameehan Mahajani

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Decoding degeneration:the implementation of machine learning for clinical detection of neurodegenerative disorders

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Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis,treatment,and tracking of complex conditions,including neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases.While no definitive methods of diagnosis or treatment exist for either disease,researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers.Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment.However,such techniques require further development aimed at improving transparency,adaptability,and reproducibility.In this review,we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer’s and Parkinson’s diseases.

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《中国神经再生研究：英文版》 |2023年第6期|1235-1242|共8页
作者
Fariha Khaliq; Jane Oberhauser; Debia Wakhloo; Sameehan Mahajani;
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作者单位

Department of Biomedical Engineering and Sciences(BMES) University of Science and Technology;

Department of Neuropathology of Medicine University;

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原文格式 PDF
正文语种 chi
中图分类神经病学与精神病学;
关键词
Alzheimer’s disease; clinical detection; deep learning; machine learning; neurodegenerative disorders; NEUROIMAGING; Parkinson’s disease;

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Decoding degeneration:the implementation of machine learning for clinical detection of neurodegenerative disorders

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