Detection of Alzheimer 's disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning
Iroshan Aberathne
Don Kulasiri
Sandhya Samarasinghe
Centre for Advanced Computational Solutions(C-fACS),Lincoln University,Christchurch,New Zealand
摘要：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.
机标关键词：alzheimerlearninganalysisdetectiondatadiseaselongitudinalmachine
论文发表日期：2023-10-28
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：7( 2134-2140 )
英文信息