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Detection of Alzheimer’s disease onset using MRI and PET neuroimaging:longitudinal data analysis and machine learning 被引量:1
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作者 iroshan aberathne Don Kulasiri Sandhya Samarasinghe 《Neural Regeneration Research》 SCIE CAS CSCD 2023年第10期2134-2140,共7页
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 effectivene... 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. 展开更多
关键词 deep learning image processing linear mixed effect model NEUROIMAGING neuroimaging data sources onset of Alzheimer’s disease detection pattern recognition
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