Artificial Intelligence Methods Based Hierarchical Classification of
  Frontotemporal Dementia to Improve Diagnostic Predictability
  Patients with Frontotemporal Dementia (FTD) have impaired cognitive
abilities, executive and behavioral traits, loss of language ability, and
decreased memory capabilities. Based on the distinct patterns of cortical
atrophy 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 purpose
of this study is to classify MRI images of every single subject into one of the
spectrums of the FTD in a hierarchical order by applying data-driven techniques
of Artificial Intelligence (AI) on cortical thickness data. This data is
computed by FreeSurfer software. We used the Smallest Univalue Segment
Assimilating Nucleus (SUSAN) technique to minimize the noise in cortical
thickness data. Specifically, we took 204 subjects from the frontotemporal
lobar degeneration neuroimaging initiative (NIFTD) database to validate this
approach, and each subject was diagnosed in one of the diagnostic categories
(bvFTD, svPPA, nfvPPA and cognitively normal). Our proposed automated
classification model yielded classification accuracy of 86.5, 76, and 72.7 with
support vector machine (SVM), linear discriminant analysis (LDA), and Naive
Bayes methods, respectively, in 10-fold cross-validation analysis, which is a
significant improvement on a traditional single multi-class model with an
accuracy of 82.7, 73.4, and 69.2.
