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Table 3 Studies selected following PRISMA guidelines presented in chronological order

From: Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review

Author (year)

Groups

Sample size (mean age)

Database

Neuroimaging technique and features

Classification method

Validation method

Results (% accuracy)

AUC ROC

Plant et al. (2010) [24]

HS

AD

MCI

18 (64.8)

32 (68.8)

24 (69.7)

Sample collected for the study

MRI: whole-brain volume measures

SVM

Bayes

VFI

Train/test method: AD + HS as train set and MCI as test set.

pMCI vs sMCI:

SVM: 50

Bayes: 58.3

VFI: 75

NA

Chincarini et al. (2011) [25]

HS

AD

sMCI

pMCI

189 (76.6)

144 (75.5)

166 (75.7)

136 (75.1)

ADNI-1

MRI: GM volumes

SVM

20-fold Cross Validation

NA

0.74

Costafreda et al. (2011) [26]

HS

AD

MCI

88 (73.6)

71 (74.9)

103 (74.1)

AddNeuroMed

MRI: 3D hippocampal morphometric measures

SVM with RBF kernel

4-fold Cross Validation

pMCI vs sMCI: 80

NA

Filipovych et al. (2011) [27]

HS

AD

sMCI

pMCI

63 (75.2)

54 (77.4)

174 (74.5)

68 (76.2)

ADNI-1

MRI: whole-brain GM density

Semi-supervised SVM

Leave-one-out Cross Validation

pMCI: 79.4

sMCI: 51.7

0.69

Hinrichs et al. (2011) [8]

HS

AD

MCI

66 (76.2)

48 (76.6)

119 (75.1)

ADNI-1

MRI and PET: scan data, APOE4 genotype, CSF assays, and cognitive tests

MK-SVM

Train/test method: AD + HS as train set and MCI as test set

pMCI vs sMCI: NA

0.79

Westman et al. (2011) [28]

HS

AD

MCI

112 (73)

117 (76)

122 (75)

AddNeuroMed

MRI: whole-brain volume, age, and education

OPLS

Train/test method: sample of 51 subjects

pMCI vs sMCI: 73

NA

Wolz et al. (2011) [29]

HS

AD

sMCI

pMCI

231 (76)

198 (75.7)

238 (74.8)

167 (74.6)

ADNI-1

MRI: hippocampal volume, cortical thickness, tensor-based morphometry, and manifold-based learning

SVM

LDA

Train/test method: 95/5 partition

pMCI vs sMCI:

SVM: 60

LDA: 68

NA

Zhang et al. (2011) [30]

HS

AD

sMCI

pMCI

52 (75.3)

51 (75.2)

56 (75.3)

43 (75.3)

ADNI-1

MRI and PET: volume, intensity, and CSF (Aβ42, t-tau y p-tau) measures

SVM

10-fold cross-validation

pMCI: 91.5

sMCI: 73.4

NA

Batmanghelich et al. (2012) [31]

sMCI

pMCI

139 (NA)

99 (NA)

ADNI-1

MRI: WM, GM, and CSF

Logistic model trees + Laplacian SVM

5-fold cross-validation

pMCI vs sMCI: 61.5

NA

Cheng et al. (2012) [32]

HS

AD

sMCI

pMCI

52 (75.3)

51 (75.2)

56 (75.3)

43 (75.3)

ADNI-1

MRI and PET: GM and WM volume, intensity, and CSF (Aβ42, t-tau y p-tau) measures

Domain Transfer SVM

Train/test method: AD + HS as train set and MCI as test set with 10-fold cross-validation

pMCI vs sMCI: 69.4

0.74

Cho et al. (2012) [33]

HS

AD

sMCI

pMCI

160 (76.2)

128 (76)

131 (74.1)

72 (74.8)

ADNI-1

MRI: cortical thickness

LDA

Train/test method: 50/50 partition

pMCI vs sMCI: 70

NA

Gray et al. (2012) [34]

HS

AD

sMCI

pMCI

54 (NA)

50 (NA)

64 (NA)

53 (NA)

ADNI-1

PET: signal intensity and relative change over 12 months

SVM with RBF kernel

Train/test method: 75/25 partition with 1000 iterations

pMCI vs sMCI: 63.1

0.66

Li et al. (2012) [35]

HS

AD

sMCI

pMCI

40 (73.7)

37 (74.8)

36 (75.3)

39 (75.6)

ADNI-1

MRI: static and dynamic cortical thickness and clustering coefficient

SVM

Leave-one-out cross-validation

pMCI vs sMCI: 81.7

NA

Toussaint et al. (2012) [36]

HS

AD

sMCI

pMCI

80 (76.4)

80 (76)

40 (76.4)

40 (76.4)

ADNI-1

PET: glucose metabolic signal and clinical measures

Two-sample t-test + spatial ICA + SVM with RBF kernel

Leave-one-out cross-validation

pMCI vs sMCI: 80

NA

Wee et al. (2012) [37]

HS

MCI

17 (72.1)

10 (74.2)

ADNI-1

MRI and PET: WM structural connectivity and GM functional connectivity

Mk-SVM

Leave-one-out cross-validation

pMCI vs sMCI: 96.3

0.95

Ye et al. (2012) [38]

sMCI

pMCI

177 (NA)

142 (NA)

ADNI-1

MRI: GM and WM volumes, cortical thickness, demographic, genetic, and cognitive measures

SVM

Leave-one-out Cross Validation

pMCI vs sMCI: NA

0.85

Zhang et al. (2012) [9]

HS

AD

sMCI

pMCI

50 (75.3)

45 (75,4)

48 (74.7)

43 (75.8)

ADNI-1

MRI and PET: volume, intensity, and CSF (Aβ42, t-tau y p-tau) measures

M3TL

10-fold cross-validation

pMCI vs sMCI: 73.9

0.80

Adaszewski et al. (2013) [39]

HS

AD

sMCI

pMCI

137 (NA)

108 (NA)

61 (74)

142 (74)

ADNI-1

ADNI-GO

MRI: whole-brain GM volume

SVM

Train/test method: AD + HS subset as train set and bootstrapping with 100 permutations

pMCI: 63.7

sMCI: NA

NA

Aguilar et al. (2013) [40]

HS

AD

sMCI

pMCI

110 (73)

116 (74.4)

98 (74.7)

21 (72.9)

AddNeuroMed

MRI: volume and cortical thickness

OPLS

SVM

Decision Trees

ANN

10-fold cross-validation

pMCI vs sMCI:

OPLS: 74.7

SVM: 70.9

Decision Trees: 67.4

ANN: 70.1

0.83

0.81

0.80

0.75

Babu et al. (2013) [41]

HS

sMCI

pMCI

232 (76)

236 (74.9)

167 (74.6)

ADNI-1

MRI: GM volumes

PBL-McqRBFN

Train/test method: 95/5 partition

pMCI vs sMCI: 79

NA

Casanova et al. (2013) [42]

HS

AD

sMCI

pMCI

188 (75.9)

171 (75.5)

182 (75.2)

153 (75)

ADNI-1

MRI: GM volumes

RLR

10-fold cross-validation

pMCI vs sMCI: 61.5

NA

Cheng et al. (2013) [43]

HS

AD

sMCI

pMCI

52 (NA)

51 (NA)

56 (NA)

43 (NA)

ADNI-1

MRI and PET: volume, intensity, APOE4 genotype, and CSF (Aβ42, t-tau y p-tau) measures

SM2TLC

10-fold cross-validation

pMCI vs sMCI: 77.8

0.81

Liu, M. et al. (2013) [44]

sMCI

pMCI

185 (74.9)

164 (74.9)

ADNI-1

MRI: GM volumes

MTSRC

Leave-one-out cross-validation

pMCI vs sMCI: 74.1

0.75

Liu, X. et al. (2013) [45]

HS

AD

sMCI

pMCI

138 (76)

86 (75)

93 (75)

97 (75)

ADNI-1

MRI: volume and cortical thickness

SVM

EN

LDA

Leave-one-out cross-validation

pMCI vs sMCI:

SVM: 66

EN: 68

LDA: 68

0.53

NA

0.68

Wee et al. (2013) [46]

HS

AD

sMCI

pMCI

200 (75.8)

198 (75.7)

111 (75.3)

89 (74.8)

ADNI-1

MRI: cortical thickness and correlation of cortical thickness between pairs of ROIs

Mk-SVM

10-fold cross-validation

pMCI vs sMCI: 75

0.84

Young et al. (2013) [47]

HS

AD

sMCI

pMCI

73 (75.9)

63 (75.2)

96 (75.6)

47 (74.5)

ADNI-1

MRI and PET: volume, intensity, APOE4 genotype, and CSF (Aβ42, t-tau y p-tau) measures

Gaussian Process

Leave-one-out cross-validation

pMCI vs sMCI: 74.1

0.80

Apostolova et al. (2014) [48]

HS

AD

MCI

111 (NA)

95 (NA)

182 (NA)

ADNI-1

MRI: hippocampal volumes and demographic, APOE genotype, and CSF measures

SVM

Leave-one-out cross-validation

pMCI vs sMCI: 68

0.68

Guerrero et al. (2014) [49]

HS

AD

sMCI

pMCI

175 (76.3)

106 (75.4)

114 (75.1)

116 (74.7)

ADNI-1

ADNI-GO

MRI: 3D brain volumes

SVM

Train/test method: unknown partition

pMCI vs sMCI: 97.2

NA

Lebedev et al. (2014) [50]

HS

AD

MCI

225 (75.9)

185 (75.2)

165 (75.5)

ADNI-1

MRI: cortical thickness, demographic variables, and APOE4 genotype

RF

Independent test set

pMCI vs sMCI: 82.3

0.83

Liu, M. et al. (2014) [51]

HS

AD

sMCI

pMCI

229 (76)

198 (75.7)

236 (74.9)

167 (74.9)

ADNI-1

MRI. whole-brain GM density

SVM

10-fold cross-validation

pMCI vs sMCI: 70.7

NA

Liu, F. et al. (2014) [52]

HS

AD

MCI

52 (75.3)

51 (75.2)

99 (75.3)

ADNI-1

MRI and PET: volume and intensity measures

Mk-SVM

10-fold cross-validation

pMCI vs sMCI: 67.8

0.70

Min et al. (2014) [53]

HS

AD

sMCI

pMCI

128 (76.1)

97 (75.9)

117 (75.1)

117 (75.2)

ADNI-1

MRI: multi-atlas GM volume measures

SVM

10-fold cross-validation

pMCI vs sMCI: 72.4

0.67

Suk et al. (2014) [54]

HS

AD

MCI

101 (75.9)

93 (75.5)

204 (74.9)

ADNI-1

MRI and PET: volume and intensity measures

DBM

10-fold cross-validation

pMCI vs sMCI: 75.9

0.75

Tong et al. (2014) [55]

HS

AD

sMCI

pMCI

231 (76)

198 (75.7)

238 (74.9)

167 (74.6)

ADNI-1

MRI: intensity patches

Multiple instance-graph

Leave-one-out cross-validation

pMCI vs sMCI: 70.4

NA

Cabral et al. (2015) [56]

sMCI

pMCI

56 (NA)

44 (NA)

ADNI-1

PET: voxel intensities

Linear-SVM

SVM-RBF

Gaussian Naïve Bayes

10-fold cross-validation

pMCI vs sMCI:

Linear-SVM: 71–89

SVM-RBF: 75–85

Gaussian Naïve Bayes: 73–81

NA

Cheng et al. (2015) [57]

HS

AD

sMCI

pMCI

52 (NA)

51 (NA)

56 (NA)

53 (NA)

ADNI-1

MRI and PET: volume, intensity and CSF (Aβ42, t-tau y p-tau) measures

M2TL

10-fold cross-validation

pMCI vs sMCI: 80.1

0.85

Cheng et al. (2015) [58]

HS

AD

sMCI

pMCI

52 (NA)

51 (NA)

56 (NA)

43 (NA)

ADNI-1

MRI and PET: volume, intensity, APOE4 genotype, and CSF (Aβ42, t-tau y p-tau) measures

Domain Transfer SVM

10-fold cross-validation

pMCI vs sMCI: 79.4

0.85

Moradi et al. (2015) [59]

HS

AD

sMCI

pMCI

231 (NA)

200 (NA)

100 (NA)

164 (NA)

ADNI-1

MRI: GM volumes, age, and cognitive measures

RF

10-fold cross-validation

pMCI vs sMCI: 81

0.90

Raamana et al. (2015) [60]

HS

AD

sMCI

pMCI

159 (NA)

136 (NA)

130 (NA)

56 (NA)

ADNI-1

MRI: cortical thickness

Variational Bayes probabilistic MKL

Train/test method: 95/5 partition

pMCI vs sMCI: 64

0.68

Ritter et al. (2015) [61]

sMCI

pMCI

151 (74.1)

86 (74.6)

ADNI-1

MRI and PET: cortical thickness, intensity measurements, neuropsychological tests, clinical variables, and demographic data

SVM with RBF kernel

Classification tree

RF

30 iterations of 10-fold cross-validation

pMCI vs sMCI: SVM: 61-64

Classification Tree: 61-64

RF: 60-61

NA

Salvatore et al. (2015) [62]

HS

AD

sMCI

pMCI

162 (76.3)

137 (76)

134 (74.5)

76 (74.8)

ADNI-1

MRI: GM and WM volumes

SVM

20-fold cross-validation

pMCI vs sMCI: 66

NA

Xu et al. (2015) [63]

HS

AD

MCI

117 (75.4)

113 (75.6)

110 (75.2)

ADNI-1

MRI and PET: volume and intensity measures

wmSRC

10-fold cross-validation

pMCI vs sMCI: 77.8

0.80

Ardekani et al. (2016) [64]

sMCI

pMCI

78 (NA)

86 (NA)

ADNI-1

MRI: hippocampal volumetric integrity, APOE genotype, demographic data, and neuropsychological tests

RF

Out-of-bag method

pMCI vs sMCI: 82.3

0.83

Collij et al. (2016) [66]

HS

AD

MCI

100 (61.7)

100 (63.1)

60 (62.7)

Sample collected for the study

MRI: whole-brain arterial spin labeling perfusion maps

SVM

Train/test method: 50/50 partition

pMCI vs sMCI: 70.8

0.77

Li et al. (2016) [67]

HS

AD

sMCI

pMCI

42 (65.6)

25 (69.4)

10 (66.5)

21 (68.6)

ADNI-1

MRI: GM whole-brain and seed-based functional connectivity

SVM

Leave one out cross-validation

pMCI vs SMCI: 80.6

NA

Liu et al. (2016) [68]

HS

AD

sMCI

pMCI

97 (75.9)

128 (76.1)

117 (75.1)

117 (75.2)

ADNI-1

MRI: GM density maps

SVM

10-fold cross-validation

pMCI vs SMCI: 79.2

0.83

López et al. (2016) [69]

sMCI

pMCI

21 (72.7)

12 (75.7)

Sample collected for the study

MRI and MEG: MEG power data on each ROI and hippocampal volumes, age, gender, cognitive reserve, neuropsychological testing score, and APOE genotype

HLR

Train/test method: 75/25 partition

pMCI vs sMCI: 100

0.97

Suk et al. (2016) [70]

HS

AD

sMCI

pMCI

52 (75.3) 51 (75.2)

56 (75)

43 (75.7)

ADNI-1

MRI and PET: GM, CSF, and intensity measures

DW-S2MTL

10-fold cross validation

pMCI vs sMCI: 73

NA

Thung et al. (2016) [71]

sMCI pMCI

53 (75.7)

60 (75.2)

ADNI-1

MRI: whole-brain GM volume and changes in 4 years of follow-up

Mk-SVM

10-fold cross validation

pMCI vs sMCI: 78.2

0.84

Vasta et al. (2016) [72]

HS

AD

sMCI

pMCI

47 (78.2)

55 (75.9)

89 (75.4)

32 (75.5)

ADNI-1

MRI: hippocampal volumes

SVM

Naive Bayes

Neural Network

Train/test method: AD + HS as train set and MCI as test set

pMCI vs sMCI:

SVM: 66.1

Naïve Bayes: 65.3

Neural Network: 66.9

NA

NA

NA

Zhang et al. (2016) [73]

sMCI

pMCI

62 (75.4)

71 (74.8)

ADNI-1

MRI: whole ventricular tensor-based morphometry

SVM

5-fold cross-validation

pMCI vs sMCI: 96.7

0.97

Zhang et al. (2016) [74]

AD

HS

MCI

194 (NA)

228 (NA)

388 (NA)

ADNI-1

MRI: multivariate hippocampal surface TBM and radial distance

AdaBoost

Leave-one-out cross-validation

pMCI vs sMCI: 77

0.75

Ҫitak-Er et al. (2017) [75]

sMCI

pMCI

165 (70.9)

140 (73.1)

ADNI-1

MRI: GM volumes

Linear SVM

Polynomial-SVM

LR

10-fold cross-validation

pMCI vs sMCI:

Linear-SVM: 73.1

Poly-SVM: 78.7

LR: 76.1

NA

Hojjati et al. (2017) [76]

sMCI

pMCI

62 (73)

18 (73.6)

ADNI-1

MRI: connectivity matrix

SVM

9-fold cross-validation

pMCI vs sMCI: 91.4

0.95

Long et al. (2017) [77]

HS

AD

sMCI

pMCI

135 (76.2)

65 (75.6)

132 (75.2)

95 (75.1)

ADNI-1

MRI: whole-brain GM and WM

SVM

10-fold cross-validation

pMCI vs sMCI:

with GM: 85.9

with WM: 68.7

GM: 0.89

WM: 0.68

Mathotaarachchi et al. (2017) [78]

sMCI

pMCI

230 (71.4)

43 (73.2)

ADNI-1

PET: intensity, demographic, and AOPE4 genotype measures

RUSRF

10-fold cross-validation

pMCI vs sMCI: 84

0.91

Suk et al. (2017) [79]

HS

AD

sMCI

pMCI

226 (NA) 186 (NA)

226 (NA)

167 (NA)

ADNI-1

MRI: GM volume

CNN

10-fold cross-validation

pMCI vs sMCI:

74.8

0.75

Tong et al. (2017) [80]

HS

AD

sMCI

pMCI

229 (75.9)

191 (75.3)

129 (74.6)

171 (74.5)

ADNI-1

MRI: global grading biomarker, age, and cognitive measures

SVM

10-fold cross-validation

pMCI vs sMCI: 81

0.87

Choi et al. (2018) [81]

HS

AD

sMCI

pMCI

182 (73.4)

139 (74.3)

92 (70.3)

79 (72.3)

ADNI-2

PET: voxel volumes of FDG and florbetapir (AV-45) images

CNN

10-fold cross-validation

pMCI vs sMCI: 84.2

0.89

Donnelly-Kehoe et al. (2018) [82]

HS

AD

sMCI

pMCI

100 (NA)

100 (NA)

100 (NA)

100 (NA)

ADNI-1

MRI: brain morphometry, demographic data, and MMSE

RF

SVM

AB

Train/test method: 75/25 partition

NA

0.75

0.76

0.63

Gao et al. (2018) [83]

HS

AD

MCI

94 (76.3)

58 (74.2)

147 (74.8)

ADNI-1

MRI and PET: hippocampal textrure features, medical history, and neuropsychological tests

GPR

PLS

Train/test method: AD + HS as train set and MCI as test set + follow-up

pMCI vs sMCI:

GPR:82.2

PLS:85.5

NA

Gómez-Sancho et al. (2018) [84]

HS

AD

sMCI

pMCI

413 (NA)

326 (NA)

173 (NA)

274 (NA)

ADNI-1

MRI: regional volumetry, surface area, and cortical thickness

SVM

RLR

10-fold cross-validation

pMCI vs sMCI:

SVM: 61-62.5

RLR: 61.1-65

0.64-0.68

0-65-0.70

Hojjati et al. (2018) [85]

sMCI

pMCI

62 (73)

18 (73.6)

ADNI-1

MRI: whole-brain cortical thickness, volumes, and connectivity matrix

SVM

9-fold cross-validation

pMCI vs sMCI: 97

0.98

Khanna et al. (2018) [86]

HS

MCI

315 (NA)

609 (NA)

ADNI-1

MRI and PET: volume, clinical, and SNP measures

GBM

10 iterations of a 10-fold cross-validation

C-index (it is a generalization of the AUC ROC calculation for binary classification): 0.86

Lin et al. (2018) [87]

HS

AD

sMCI

pMCI

229 (NA)

188 (NA)

139 (NA)

169 (NA)

ADNI-1

MRI: hippocampal measures and whole-brain cortical volume, surface area, and cortical thickness

LASSO + ELM

Leave-one-out cross-validation

pMCI vs sMCI: 79.9

0.86

Liu et al. (2018) [88]

HS

AD

sMCI

pMCI

230 (77.1)

200 (76.6)

160 (76.2)

120 (75.9)

ADNI-1

MRI: whole-brain hierarchical structural network

Multiple Kernel Boost

10-fold cross-validation

pMCI vs sMCI: 72.8

0.72

Liu et al. (2018) [89]

HS

AD

MCI

126 (76)

186 (75.4)

395 (74.9)

ADNI-1

MRI and PET: volume, intensity, and CSF (Aβ42, t-tau y p-tau) measures

Multi-hyper graph Learning

10-fold cross-validation

pMCI vs sMCI: 74.7

0.72

Lu et al. (2018) [90]

HS

AD

sMCI

pMCI

360 (73.4)

238 (75)

409 (74)

217 (74)

ADNI-1

MRI and PET: volume, intensity, and CSF (Aβ42, t-tau y p-tau) measures

Deep NN

10-fold cross-validation

pMCI vs sMCI: 82.9

NA

Minhas et al. (2018) [91]

sMCI

pMCI

54 (74.1)

65 (74.7)

ADNI-1

MRI: whole-brain volumes, surface area, cortical thickness, and neuropsychological measures

SVM

5-fold cross-validation

pMCI vs sMCI: 84.3

0.89

Popuri et al. (2018) [92]

sHS

uHS

pSH

pMCI

sMCI

eDAT

sDAT

360 (75.4)

52 (78.9)

18 (78.2)

205 (74.8)

431 (75)

133 (76.6)

238 (75.8)

ADNI-1

PET: glucose metabolic signal

FPDS

Independent test set

Classification of DAT+/DAT-:

pMCI = 67.9

sMCI = 70.4

pMCI vs sMCI at 2, 3, and 5 years:

0.81

0.80

0.77

Sorensen et al. (2018) [93]

HS

AD

sMCI

pMCI

100 (NA)

100 (NA)

100 (NA)

100 (NA)

ADNI-1

MRI: brain volumetry, cortical thickness, WM hipointensities, MMSE, age, and hippocampal sub-regional volumetry

Linear SVM

RBF SVM

Train/test method: 60/40 partition

pMCI vs sMCI:

Linear SVM: 55.6

RBF SVM: 55

NA

NA

Sun et al. (2018) [94]

HS

AD

sMCI

pMCI

162 (76.3)

137 (76)

134 (74.5)

76 (74.8)

ADNI-1

MRI: GM densities

LASSO + SVM

Train/test method: 50/50 partition

pMCI vs sMCI: 65.4

0.68

Wu et al. (2018) [95]

HS

sMCI

pMCI

150 (75.6)

150 (75.3)

157 (75.3)

ADNI-1

MRI: 3D brain volumes

GoogleNet

CaffeNet

5-fold cross-validation

GoogleNet/CaffeNet in: pMCI: 84.7/92.3

sMCI: 67.3/72

NA

Yan et al. (2018) [96]

sMCI

pMCI

50 (NA)

29 (NA)

ADNI-1

PET: 3D images

ResNet

10-fold cross-validation

pMCI vs sMCI: 82

0.81

Basaia et al. (2019) [97]

HS

AD

sMCI

pMCI

352 (74.5)

294 (75.1)

510 (72.3)

253 (73.8)

ADNI-1

MRI: WM, GM, and CSF measures

CNN

Independent test set

pMCI vs sMCI: 74.9

NA

Cheng et al. (2019) [98]

HS

AD

MCI

112 (NA)

102 (NA)

192 (NA)

ADNI-1

MRI: GM volumes and CSF measures

SVM

10-fold cross-validation

pMCI vs sMCI: 76.3

0.81

Collazos-Huertas et al. (2019) [99]

sMCI

pMCI

325 (75)

245 (75)

ADNI-1

MRI: volumes and cortical thickness

SVM

KNN

5-fold cross-validation

pMCI vs sMCI:

SVM: 76.1

KNN: 77.8

NA

NA

Elahifasaee et al. (2019) [100]

HS

AD

sMCI

pMCI

229 (76)

198 (57.5)

236 (74.9)

167 (74.9)

ADNI-1

MRI: GM density

KDA

10-fold cross-validation

pMCI vs sMCI: 65.9

0.71

Ezzati et al. (2019) [101]

HS

AD

sMCI

pMCI

424 (74.3)

249 (74.7)

372 (72.8)

235 (73)

ADNI-1

MRI: whole-brain volumes, APOE4 genotype, and demographic measures

Ensemble Learning

10-fold cross-validation

MCI to AD at 6, 12,

24, 36, and 48 months: 63.8%, 68.9%, 74.9%, 75.3%, and 77%, respectively

NA

Gupta et al. (2019) [102]

HS

AD

sMCI

pMCI

38 (76.7)

38 (77.1)

36 (74.2)

46 (76.7)

ADNI-1

MRI and PET: whole-brain volume, intensity and CSF (Aβ42, t-tau y p-tau) measures

Mk-SVM

10-fold cross-validation

pMCI vs sMCI: 94.9

0.94

Lee et al. (2019) [103]

HS

AD

sMCI

pMCI

415 (NA)

338 (NA)

558 (NA)

307 (NA)

ADNI-1

MRI: brain phenotypes, demographic and neuropsychological data, APOE4 genotype, and CSF measures

rDNN

5-fold cross-validation

pMCI vs sMCI at 6, 12, 18 and 24 months: 81, 81, 79 and 80 respectively

NA

Lee et al. (2019) [104]

HS

AD

sMCI

pMCI

229 (76)

198 (75.4)

214 (75)

160 (74.9)

ADNI-1

MRI: GM volumes

rDNN + SVM

10-fold cross-validation

pMCI vs sMCI: 88.5

0.96

Lei et al. (2019) [105]

HS

AD

sMCI

pMCI

152 (NA)

91 (NA)

98 (NA)

104 (NA)

ADNI-1

MRI: GM volumes and neuropsychological measures

SVR

10-fold cross-validation

pMCI vs sMCI: 78

NA

Li et al. (2019) [106]

NA

803 (NA)

ADNI-1/2/GO and AIBL

MRI: hippocampal volumes, cognitive, demographic and neuropsychological measures

NN

Train in ADNI-1 and test in ADNI-2&GO

MCI to AD prediction (C-index): 0.86

Li et al. (2019) [107]

HS

AD

sMCI

pMCI

165 (76.4)

142 (76.1)

95 (74.9)

126 (73.4)

ADNI-1

MRI: cortical thickness and volumes

SVM with RBF kernel

10-fold cross-validation

pMCI vs sMCI: 69.8

0.70

Oh et al. (2019) [108]

HS

AD

sMCI

pMCI

230 (76)

198 (75.6)

101 (74.1)

166 (74.8)

ADNI-1

MRI data

CNN

5-fold cross-validation

pMCI vs sMCI: 73.9

NA

Pan et al. (2019) [109]

HS

AD

sMCI

pMCI

90 (76.1)

94 (75.8)

44 (77.6)

44 (75.9)

ADNI-1

PET: intensities and connectivity measures

SVM

10-fold cross-validation

pMCI vs sMCI: 72.3

0.72

Pusil et al. (2019) [110]

sMCI

pMCI

27 (71.2)

27 (74.8)

Sample collected for the study

MEG: brain connectivity matrix

MCFS + SVM with RBF kernel

Train/test method: 80/20 partition

pMCI vs sMCI: 100

NA

Spasov et al. (2019) [111]

HS

AD

sMCI

pMCI

184 (74.6)

192 (75.6) 228 (72.2)

181 (73.7)

ADNI-1

MRI: brain volumes, demographic, neuropsychological, and genetic (APOE4) measures

CNN

Train/test method: 90/10 partition

pMCI vs sMCI: 86

0.93

Wang et al. (2019) [112]

HS

AD

MCI

71 (72.5)

48 (75)

60 (72.6)

ADNI-2

MRI: morphometry and WM structural connectivity

LR

10-fold cross-validation

pMCI vs sMCI: 59

0.65

Wee et al. (2019) [113]

HS

AD

MCI

eMCI

lMCI

ADNI-1/ADNI-2:

242/300 (76.9/75.6)

355/261 (76.3/75.3)

415/NA (75.9)

NA/314 (72.9)

NA/208 (73.7)

MRI: cortical thickness

Graph NN

10-fold cross-validation

Conversion from:

eMCI to AD: 79.2

lMCI to AD: 65.2

NA

Xu et al. (2019) [114]

HS

AD

sMCI

pMCI

165 (76.4)

142 (76.1)

95 (74.9)

126 (73.4)

ADNI-1

MRI: cortical thickness

SVM with RBF kernel

10-fold cross-validation

pMCI vs sMCI: 63.7

0.67

Zhou et al. (2019) [115]

HS

AD

sMCI

pMCI

204 (76.1)

171 (75.5)

205 (75.1)

157 (74.8)

ADNI-1

MRI and PET: GM volumes, average intensities and SNP measures

Multi-modal Classifier

10-fold cross-validation

pMCI vs sMCI: 74..3

0.75

Zhu et al. (2019) [116]

HS

AD

MCI

101 (75.8)

93 (75.4)

202 (75.1)

ADNI-1

MRI and PET: GM volumes and average intensities

SVM

10-fold cross-validation

pMCI vs sMCI: 72.6

0.73

Abrol et al. (2020) [117]

HS

AD

sMCI

pMCI

237 (74.3)

157 (75.1)

245 (72.1)

189 (74.2)

ADNI-1

ADNI-2

ADNI-3

ADNI-GO

MRI: 3D brain volumes

ResNet

Train/test method: 80/20 partition

pMCI vs sMCI: 75.1

0.78

Gao et al. (2020) [118]

HS

sMCI

pMCI

847 (56.9)

129 (74.8)

168 (74.8)

ADNI-1

MRI: 3D brain volumes

Age prediction + AD-NET

5-fold cross-validation

pMCI vs sMCI; 76

0.81

Giorgio et al. (2020) [119]

HS

MCI

317 (NA)

272 (NA)

ADNI-1

MRI and PET: GM density, genetic, and cognitive measures

GMLVQ

10-fold cross-validation

pMCI vs sMCI: 81.4

NA

Khatri et al. (2020) [120]

HS

AD

MCI

57 (75.6)

53 (74.4)

77 (74.1)

ADNI-1

MRI: cortical thickness,

surface area, GM volumes, MMSE, APOE4 data, and levels of Aβ42, T-tau and P-tau in CSF

SVM with RBFk

Linear SVM

ELM

10-fold cross-validation

pMCI vs sMCI:

SVM-RBFk: 71.3

Linear SVM: 75.7

ELM: 83.4

NA

NA

0.85

Lin et al. (2020) [121]

HS

AD

sMCI

pMCI

200 (73.9)

102 (75.7)

205 (71.8)

110 (73.9)

ADNI-1

MRI and PET: volume, cortical thickness, intensity measures, APOE4 presence, and levels of Aβ42, T-tau, and P-tau in CSF

LASSO + ELM with Gaussian kernel

10-fold cross-validation

pMCI vs sMCI: 84.7

0.89

Lin et al. (2020) [122]

sMCI

pMCI

124 (70.8)

40 (71.6)

ADNI-1

MRI: GM densities

SVM

4-fold cross-validation

pMCI vs sMCI: 97.3

0.98

Pan et al. (2020) [123]

HS

AD

sMCI

pMCI

262 (NA)

237 (NA)

173 (NA)

115 (NA)

ADNI-1

MRI: 3D brain volumes

CNN + EL

5-fold cross-validation on independent test set

pMCI vs sMCI: 62

0.59

Ramon-Julvez et al. (2020) [124]

HS

AD

sMCI

pMCI

181 (NA)

191 (NA)

227 (NA)

179 (NA)

ADNI-1

MRI data and Jacobian determinant of diffeomorphic transformations

CNN

10-fold cross-validation

pMCI vs sMCI: 89

0.94

Xiao et al. (2020) [125]

HS

AD

sMCI

pMCI

50 (77.8)

51 (75.8)

45 (71.9)

51 (72.5)

ADNI-1

MRI: GM volumes

LR

10-fold cross-validation

pMCI vs sMCI: 72.9

NA

Xu et al. (2020) [126]

HS

MCI

53 (69.6)

76 (73.7)

Sample collected for the study

MEG: brain connectivity matrix

MG2G Embedding model

Train/validation/test method: 85/10/5 partition

HS vs pMCI vs sMCI: 82

pMCI vs sMCI: 87

0.75-0.96

Yang et al. (2020) [127]

sMCI

pMCI

280 (72)

70 (71.7)

ADNI-1

PET: GM densities

CNN + SVM

Train/test method: unknown partition

pMCI vs sMCI: 78.6

NA

Yee et al. (2020) [128]

sHS

uHS

pSH

pMCI

sMCI

eDAT

sDAT

359 (75.4)

51 (79)

19 (78.1)

210 (75)

427 (75)

135 (76.6)

237 (75.7)

ADNI-1

PET: intensity measures

CNN

5-fold cross-validation

pMCI vs sMCI: 74.7

0.81

Zhou et al. (2020) [129]

HS

AD

sMCI

pMCI

226 (75.8)

186 (75.3)

205 (75.1)

157 (74.7)

ADNI-1

MRI and PET: GM volumes and intensity measures

SVM

10-fold cross-validation

pMCI vs sMCI: 74-76

0.74-0.76

Bae et al. (2021) [130]

HS

AD

sMCI

pMCI

2084 (76.5)

1406 (76.2)

222 (72.2)

228 (74.2)

ADNI-1

MRI: 3D brain volume and neuropsychological measures

CNN

Train/validation/test method: 70/15/15 partition

pMCI vs sMCI: 82.4

NA

Mofrad et al. (2021) [131]

sMCI

pMCI

333 (NA)

134 (NA)

ADNI-1

MRI: hippocampal entorhinal cortex, ventricles, and neuropsychological measures

Ensemble Learning

15-fold cross-validation

pMCI vs sMCI: 77

NA

Mofrad et al. (2021) [132]

sMCI

pMCI

279 (NA)

279 (NA)

ADNI-1 and AIBL

MRI: hippocampal and ventricle measures

Ensemble Learning

15-fold cross-validation

pMCI vs sMCI: 78

NA

Pan et al. (2021) [133]

HS

AD

sMCI

pMCI

242 (73.6)

237 (75)

360 (71.7)

166 (73.9)

ADNI-1

PET: 3D images

CNN

5-fold cross-validation repeated 2 times

pMCI vs sMCI: 83

0.87

Shen et al. (2021) [134]

HS

AD

sMCI

pMCI

150 (NA)

143 (NA)

89 (NA)

86 (NA)

ADNI-1

MRI and PET: Volume, cortical thickness, intensity measures, APOE4 presence and levels of Aβ42, T-tau, and P-tau in CSF

SVM

10-fold cross-validation

pMCI vs sMCI: 75-78

0.76-0.80

Syaifullah et al. (2021) [135]

HS

AD

MCI

543 (NA)

359 (NA)

544 (NA)

NA-ADNI

MRI and PET data and MMSE

SVM

Train/test method: 50/50 partition

pMCI vs sMCI: 87.9

NA

Wen et al. (2021) [136]

HS

AD

MCI

sMCI

pMCI

46 (72.7)

46 (74.4)

97 (72.9)

54 (72.6)

24 (74.2)

ADNI-1

MRI: GM density

SVM

10-fold cross-validation

pMCI vs sMCI: 80

NA

Zhang et al. (2021) [137]

HS

AD

sMCI

pMCI

275 (76.2)

280 (76.1)

251 (77.6)

162 (75.1)

ADNI-1

MRI: 3D brain volumes

CNN

Train/validation/test method: 70/15/15 partition

pMCI vs sMCI: 78.8

0.87

Zhu et al. (2021) [138]

HS

AD

sMCI

pMCI

275 (76.2)

280 (76.1)

251 (77.6)

162 (75.1)

ADNI-1

MRI: GM, WM, and CSF measures, demographic data and APOE genotype

Temporally structured-SVM

10-fold cross-validation

pMCI vs sMCI: 85.4

0.86

  1. Note. AB Ada-Boost, AD Alzheimer’s disease, AD-NET Age-adjust neural network, AIBL Australian Imaging, Biomarkers and Lifestyle Flagship Study of Aging, ANN Artificial neural network, AUC Area under the curve, CNN Convolutional neural network, DAT Dementia Alzheimer type, DBM Deep Boltzmann Machine, DW-S2MTL Deep-weighted subclass-based sparse multi-task learning, EL Ensemble learning, eDAT Early DAT, ELM Extreme learning machine, eMCI Early MCI, EN Elastic nets, F-FDG Fluorine 18 fluorodesoxyglucose, FPDS FDG-PET, GM Gray matter, GMB Gradient boosting model, GPR Gaussian process regression, HS Healthy subjects, HLR Hierarchical logistic regression, ICA Independent component analysis, KDA Kernel discriminant analysis, lMCI Late MCI, LR Logistic regression, M2TL Multimodal manifold-regularized transfer learning, M3TL Multi-modal multi-task learning, MCI Mild cognitive impairment, MCFS Multi-cluster feature selection, MG2G Multiple Graph2Gauss, MKL Multiple kernel learning, MMSE Mini Mental State Examination, MTSRC Multi-task sparse representation classifier, NA Not applicable, nl-SVM-RBFk Non-linear SVM with radial basis function kernel, NN Neural network, OPLS Orthogonal partial least squares, PBL-McqRBFN Projection-based learning for meta-cognitive q-Gaussian radial basis function network, PLS Partial least squares, pMCI Progressive MCI, rDNN Randomized deep neural network, Res-Net deep residual neural network, RF Random forest, RLR Regularized logistic regression, RUSRF Random under sampled random forest, sDAT Stable DAT, SM2TLC Sparse multimodal manifold-regularized transfer learning classification, sMCI Stable MCI, SNN Spiking neural network, SNP Single-nucleotide polymorphisms, ss Sample selection, SVM Support vector machine, VFI Voting feature intervals, WM White matter, wmSRC Weighted multi-modality sparse representation-based classification