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• W4206696543 abstract "Alzheimer's disease (AD) is the most prevalent form of dementia. The accurate diagnosis of AD, especially in the early phases is very important for timely intervention. It has been suggested that brain atrophy, as measured with structural magnetic resonance imaging (sMRI), can be an efficacy marker of neurodegeneration. While classification methods have been successful in diagnosis of AD, the performance of such methods have been very poor in diagnosis of those in early stages of mild cognitive impairment (EMCI). Therefore, in this study we investigated whether optimisation based on evolutionary algorithms (EA) can be an effective tool in diagnosis of EMCI as compared to cognitively normal participants (CNs). Structural MRI data for patients with EMCI (n = 54) and CN participants (n = 56) was extracted from Alzheimer's disease Neuroimaging Initiative (ADNI). Using three automatic brain segmentation methods, we extracted volumetric parameters as input to the optimisation algorithms. Our method achieved classification accuracy of greater than 93%. This accuracy level is higher than the previously suggested methods of classification of CN and EMCI using a single- or multiple modalities of imaging data. Our results show that with an effective optimisation method, a single modality of biomarkers can be enough to achieve a high classification accuracy." @default.
• W4206696543 created "2022-01-26" @default.
• W4206696543 creator A5005126288 @default.
• W4206696543 creator A5038255157 @default.
• W4206696543 creator A5039719543 @default.
• W4206696543 date "2022-01-19" @default.
• W4206696543 modified "2023-10-18" @default.
• W4206696543 title "Diagnosis of early mild cognitive impairment using a multiobjective optimization algorithm based on T1-MRI data" @default.
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• W4206696543 doi "https://doi.org/10.1038/s41598-022-04943-3" @default.
• W4206696543 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35046444" @default.
• W4206696543 hasPublicationYear "2022" @default.
• W4206696543 type Work @default.
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