Table 3.
Studies selected following PRISMA guidelines presented in chronological order
| 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 |
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