中国神经再生研究(英文版) ›› 2023, Vol. 18 ›› Issue (10): 2134-2140.doi: 10.4103/1673-5374.367840

• 综述:退行性病与再生 • 上一篇    下一篇

利用MRI和PET神经影像检测阿尔茨海默病的发病情况:纵向数据分析和机器学习

  

  • 出版日期:2023-10-15 发布日期:2023-03-28

Detection of Alzheimer’s disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning

Iroshan Aberathne, Don Kulasiri*, Sandhya Samarasinghe   

  1. Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Christchurch, New Zealand
  • Online:2023-10-15 Published:2023-03-28
  • Contact: Don Kulasiri, PhD, Don.Kulasiri@lincoln.ac.nz.

摘要: https://orcid.org/0000-0001-8744-1578 (Don Kulasiri)

Abstract: The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.

Key words: deep learning, image processing, linear mixed effect model, neuroimaging, neuroimaging data sources, onset of Alzheimer’s disease detection, pattern recognition