Decoding degeneration:the implementation of machine learning for clinical detection of neurodegenerative disorders
Fariha Khaliq1
Jane Oberhauser2
Debia Wakhloo3
Sameehan Mahajani4
1.Department of Biomedical Engineering and Sciences(BMES),National University of Science and Technology,Islamabad,Pakistan2.Department of Neuropathology,School of Medicine,Stanford University,Stanford,CA,USA3.Department of Neuropathology,School of Medicine,Stanford University,Stanford,CA,USA;Levitas Bio,Menlo Park,CA,USA4.Department of Neuropathology,School of Medicine,Stanford University,Stanford,CA,USA;CODA biotherapeutics,South San Francisco,CA,USA
摘要：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.
机标关键词：learningclinicalneuroddetectiondecodingdegenerationdegenerativedisorders
论文发表日期：2023-06-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：8( 1235-1242 )
英文信息