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. 2021 Jun 5;18(11):6099. doi: 10.3390/ijerph18116099

Table 3.

Summary of work and predictions relating to the detection of SZ using data from diffusion-weight MRI, diffusion tensor imaging and perfusion MRI scans via various artificial intelligence techniques and machine learning algorithms.

Study Year Subjects Prediction AI/ML Technique
Patients Control
Caan et al. [111] 2006 34♂ 24 (not reported) LDA, PCA
Caprihan et al. [112] 2008 45 45 (age-matched) 100% DPCA
Ingalhalikar et al. [113] 2010 27♀ 37♀ 90.62% Nonlinear SVM
Rathi et al. [114] 2010 21 (FEP) 20 (age-matched) SH: 78% (sensitivity)
80% (specificity)
F2T: 86% (sensitivity)
85% (specificity)
K-nearest neighbours, Parzen window classifier, SVM
Ardekani et al. [115] 2011 50 50 (age- and sex-matched) FA: 96% (sensitivity)
92% (specificity)
MD: 96% (sensitivity)
100% (specificity)
Fisher’s LDA
Squarcina et al. [116] 2015 35 (FEP) 35 83% SVM