Table 8 Summary of the available literature on machine learning algorithms for binary classification of (I123)FP-CIT images since 2010, ordered according to maximum accuracy (where available)

Augimeri et al. 2016 [30]

Mean ellipsoid uptake, dysmorphic index (ellipsoid orientation)

SVM

43 local images (12 normal, 31 Parkinson’s disease (PD)), no cross-validation mentioned

Up to 100% accuracy, specificity and sensitivity

Bhalchandra et al. 2015 [31]

Analysis of 42nd slice only. Striatal binding ratios in both caudates and putamina, radial features and gradient features. Features are tested for statistical significance (wilcoxon rank) before use in the classifier

Linear SVM and SVM with Radial Basis Function (RBF) kernel, Linear Discriminant Analysis (LDA)

350 images from PPMI database (187 healthy controls (HC), 163 PD). 5 fold cross-validation (CV), repeated 100 times

Linear SVM: maximum of accuracy = 99.4%

RBF kernel: maximum of accuracy = 99.4%

LDA: maximum of accuracy = 99.4%

Oliveira and Castelo-Branco 2015 [32]

Image voxels within striatal region of interest

Linear SVM

654 images from PPMI database (209 HC, 445 PD). Leave-one-out CV

Maximum of accuracy = 97.9%

Sensitivity = 97.8%

Specificity = 98.1%

Prashanth et al. 2017 [33]

16 shape and 14 surface fitting features of selected slices, following thresholding. Striatal binding ratios of both caudates and putamina and asymmetry indices were also considered. Features are tested for statistical significance (wilcoxon rank) before use in the classifier

SVM with RBF kernel, boosted trees, random forests, naive bayes

715 images from PPMI database (208 HC, 427 PD, 80 scans without evidence of dopaminergic deficit (SWEDD)). 10 fold CV, repeated 100 times. Hyperparameters for SVM chosen through 10 fold CV

SVM: accuracy = 97.3 ± 0.1%

Sensitivity = 97.4 ± 0.1%

Specificity = 97.2 ± 0.2%

Boosted trees: accuracy = 96.8 ± 0.2%

Sensitivity = 97.1 ± 0.3%

Specificity = 96.3 ± 0.4%

Random forests: accuracy = 96.9 ± 0.2%

Sensitivity = 97.2 ± 0.2%

Specificity = 96.5 ± 0.3%

Naive Bayes: accuracy = 96.9 ± 0.1%

Sensitivity = 96.4 ± 0.1%

Specificity = 96.5 ± 0.2%

Tagare et al. 2017 [34]

Voxel intensities within a region of interest

Logistic lasso

658 images from PPMI database (210 HC, 448 PD). 3 fold CV for performance assessment. Parameters chosen through 10 fold CV (nested within outer 3 fold CV).

Maximum of accuracy = 96.5 ± 1.3%

Palumbo et al. 2014 [35]

Striatal binding ratios for both caudates and putamina (and a subset of these 4 features), patient age

SVM with RBF kernel

90 local images from patients with ‘mild’ symptoms (34 non-PD, 56 PD). Leave-one-out and 5 fold CV

Maximum of accuracy = 96.4%

Prashanth et al. 2014 [36]

Striatal binding ratio for both caudates and putamina

SVM, linear and RBF kernel.

493 images from PPMI database (181 HC, 369 early PD), 10 fold CV, no repeats

RBF kernel: accuracy = 96.1%, sensitivity = 96.6%, specificity = 95.0%

Linear SVM: accuracy = 92.3%, sensitivity = 95.3%, specificity = 84.0%

Martinez-Murcia et al. 2013 [37]

12 Haralick texture features within a brain region of interest

Linear SVM

‘Whole’ PPMI database. Leave-one-out CV

Maximum of accuracy = 95.9%, sensitivity = 97.3%, specificity = 94.9%

Zhang and Kagen 2016 [38]

Voxel intensities from a single axial slice, repeated for 3 different slices

Single layer Neural network

1513 images from PPMI database (baseline and follow-up, 1171 PD, 211 HC, 131 SWEDD). 1189 images for training, 108 for validation, 216 for testing. 10 fold CV

Maximum of accuracy = 95.6 ± 1.5%, sensitivity = 97.4 ± 4.3%, specificity = 93.1 ± 3.6%

Rojas et al. 2013 [39]

Voxel intensities, independent component analysis (ICA) & principal component analysis (PCA) decomposition of voxel data (after applying empirical mode decomposition) within regions of interest

Linear SVM

80 local images (39 non-pre-synaptic dopaminergic deficit (non-PDD), 41 PDD). Leave-one-out CV

Raw voxels: accuracy = 87.5%, sensitivity = 90.2%, specificity = 84.6%

ICA features: maximum of accuracy = 91.2%, sensitivity = 91.8%, specificity = 92.9%

PCA features: maximum of accuracy = 95.0%, sensitivity = 95.1%, specificity = 94.9%

Towey et al. 2011 [12]

PCA decomposition of voxels within striatal region of interest

Naïve-Bayes, Group prototype

116 local images (37 non-PDD, 79 PDD). Leave-one-out CV

Naïve-Bayes: accuracy = 94.8%, sensitivity = 93.7%, specificity = 97.3%

Group prototype: accuracy = 94.0%, sensitivity = 93.7%, specificity = 94.6%

Segovia et al. 2012 [40]

Partial least squares decomposition of voxels within striatal regions

SVM applied to hemispheres separately. RBF kernel

189 local images (94 non-PDD, 95 PDD). Leave-one-out CV

Features varied from 1 to 20. Maximum of accuracy = 94.7%, sensitivity = 93.2%, specificity = 93.6%

Martinez-Murcia et al. 2014 [41]

ICA decomposition of selected voxels

SVM, linear and RBF kernel

208 local images (100 non-PDD, 108 PDD), 289 images from PPMI database (114 normal, 175 PD). 30 fold CV

RBF kernel: maximum of accuracy = 94.7%, sensitivity = 98.1%, specificity = 92.0%

Linear SVM: maximum of accuracy = 92.8%, sensitivity = 98.2%, specificity = 93.0%

Illan et al. 2012 [42]

Image voxel intensities and image voxels within striatal region of interest

Nearest mean, k-nearest neighbour (k-NN), linear SVM

208 local images (108 non-PDD, 108 PDD). 30 random permutations CV, with 1/3 data held out for testing

SVM: maximum of sensitivity = 89.0%, specificity = 93.2%

Nearest mean: maximum of sensitivity = 90.7%, specificity = 84.0%

k-NN: maximum of sensitivity = 88.6%, specificity = 86.9%

Palumbo et al. 2010 [43]

Striatal binding ratios for caudate and putamina on 3 slices

Probablistic neural network (PNN), Classification tree (CT)

216 local images (89 non-PDD, 127 PD). Two fold CV, repeated 1000 times

PNN: for patients with essential tremor mean probability of correct classification = 96.6 ± 2.6%

CT: for patients with essential tremor mean probability of correct classification = 93.5 ± 3.4%