Abstract
Clinical trials for interventions that seek to delay the onset of Alzheimer’s disease (AD) are hampered by inadequate methods for selecting study subjects who are at risk, and who may therefore benefit from the interventions being studied. Automated monitoring tools may facilitate clinical research and thereby reduce the impact of AD on individuals, caregivers, society at large, and government healthcare infrastructure. We studied the 18F-deoxyglucose positron emission tomography (FDG-PET) scans of research subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), using a Machine Learning technique. Three hundred ninety-four FDG-PET scans were obtained from the ADNI database. An automated procedure was used to extract measurements from 31 regions of each PET surface projection. These data points were used to evaluate the sensitivity and specificity of support vector machine (SVM) classifiers and to compare both Linear and Radial-Basis SVM techniques against a classic thresholding method used in earlier work.
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Benton, R.G., Choubey, S., Clark, D.G., Johnsten, T., Raghavan, V.V. (2013). Diagnosis and Grading of Alzheimer’s Disease via Automatic Classification of FDG-PET Scans. In: Imamura, K., Usui, S., Shirao, T., Kasamatsu, T., Schwabe, L., Zhong, N. (eds) Brain and Health Informatics. BHI 2013. Lecture Notes in Computer Science(), vol 8211. Springer, Cham. https://doi.org/10.1007/978-3-319-02753-1_27
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DOI: https://doi.org/10.1007/978-3-319-02753-1_27
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