Classification of Alzheimer’s Disease from ¹⁸F-FDG and ¹¹C-PiB PET Imaging Biomarkers Using Support Vector Machine

Purpose: Alzheimer’s disease (AD) is the most common type of dementia, and its early diagnosis has become a crucial issue. Machine learning provides a systematic and objective approach in classification. Currently, there are many studies using several kinds of neuroimaging modalities to perform classification in dementia. Support vector machine (SVM) is one of machine learning based classification algorithm which is able to retain favorable classification accuracy even with small sample sizes. Our aim is to investigate the feasibility of using dual PET biomarkers in combination with SVM for AD diagnosis in small sample sizes. Methods: This study collected PET (¹⁸F-FDG and ¹¹C-PiB) and T1 MRI image of 79 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, including 20 AD, 27 mild cognitive impairment (MCI) subjects, and 32 normal controls (NCs), and performed classification using the SVM algorithm with the quantification of the two PET biomarkers, and finally compared the classification results of each brain region. Results: In the classification between diseased (AD and MCI) and NC group, we found that the accuracy, sensitivity and specificity mean in temporal cortex are the highest. Conclusions: Overall, using dual PET biomarkers in combination with SVM shows a certain feasibility and clinical value in the diagnosis of AD, especially in the temporal cortex.