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Patients with Frontotemporal Dementia (FTD) have impaired cognitiveabilities, executive and behavioral traits, loss of language ability, anddecreased memory capabilities. Based on the distinct patterns of corticalatrophy and symptoms, the FTD spectrum primarily includes three variants:behavioral variant FTD (bvFTD), non-fluent variant primary progressive aphasia(nfvPPA), and semantic variant primary progressive aphasia (svPPA). The purposeof this study is to classify MRI images of every single subject into one of thespectrums of the FTD in a hierarchical order by applying data-driven techniquesof Artificial Intelligence (AI) on cortical thickness data. This data iscomputed by FreeSurfer software. We used the Smallest Univalue SegmentAssimilating Nucleus (SUSAN) technique to minimize the noise in corticalthickness data. Specifically, we took 204 subjects from the frontotemporallobar degeneration neuroimaging initiative (NIFTD) database to validate thisapproach, and each subject was diagnosed in one of the diagnostic categories(bvFTD, svPPA, nfvPPA and cognitively normal). Our proposed automatedclassification model yielded classification accuracy of 86.5, 76, and 72.7 withsupport vector machine (SVM), linear discriminant analysis (LDA), and NaiveBayes methods, respectively, in 10-fold cross-validation analysis, which is asignificant improvement on a traditional single multi-class model with anaccuracy of 82.7, 73.4, and 69.2.

Artificial Intelligence Methods Based Hierarchical Classification of Frontotemporal Dementia to Improve Diagnostic Predictability