Early Alzheimer's Disease Detection: A Review of Machine Learning Techniques for Forecasting Transition from Mild Cognitive Impairment

被引:3
作者
Singh, Soraisam Gobinkumar [1 ]
Das, Dulumani [1 ]
Barman, Utpal [1 ]
Saikia, Manob Jyoti [2 ,3 ]
机构
[1] Assam Down Town Univ, Fac Comp Technol, Gauhati 781026, Assam, India
[2] Univ North Florida, Biomed Sensors & Syst Lab, Jacksonville, FL 32224 USA
[3] Univ North Florida, Dept Elect Engn, Jacksonville, FL 32224 USA
关键词
Alzheimer's disease; mild cognitive impairment; AD; MCI; MRI; PET; machine learning; EARLY-DIAGNOSIS; AD; CLASSIFICATION; FUSION;
D O I
10.3390/diagnostics14161759
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Alzheimer's disease is a weakening neurodegenerative condition with profound cognitive implications, making early and accurate detection crucial for effective treatment. In recent years, machine learning, particularly deep learning, has shown significant promise in detecting mild cognitive impairment to Alzheimer's disease conversion. This review synthesizes research on machine learning approaches for predicting conversion from mild cognitive impairment to Alzheimer's disease dementia using magnetic resonance imaging, positron emission tomography, and other biomarkers. Various techniques used in literature such as machine learning, deep learning, and transfer learning were examined in this study. Additionally, data modalities and feature extraction methods analyzed by different researchers are discussed. This review provides a comprehensive overview of the current state of research in Alzheimer's disease detection and highlights future research directions.
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页数:29
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