Table 1 Overview of previous studies applying neural networks for the detection of AD and MCI
Study (chronologic order) | Data type | Sample | Algorithm | Performance | Addressed model comprehensibility | |||||
|---|---|---|---|---|---|---|---|---|---|---|
AD | MCI c/nc | CN | Groups | Accuracy | Balanced accuracy | AUC | ||||
Suk et al. [17] | MRI GM and FDG-PET | 93 | 76/128 | 101 | RBM on class discriminative patches selected by statistical significance tests | AD/CN MCI/CN MCIc/MCInc | 95.4% 85.7% 74.6% | 94.9% 80.6% 71.6% | 0.988 0.881 0.747 | Visualization of selected features (image patches) and RBM model weights projected on MRI scan |
Li et al. [18] | MRI and FDG-PET | 51 | 43/56 | 52 | RBM for feature learning, SVM for classification | AD/CN MCI/CN MCIc/MCInc | 91.4% 77.4% 57.4% | No | ||
Ortiz et al. [19] | MRI GM and FDG-PET | 70 | 39/64 | 68 | RBM for feature learning, SVM for classification | AD/CN MCIc/CN MCIc/MCInc | 90% 83% 78% | 0.95 0.95 0.82 | Visualization of SVM model weights projected on MRI scan | |
Aderghal et al. [20] | MRI and DTI | 188 | 339 | 228 | CNN for hippocampus region of interest only | AD/CN MCI/CN | 92.5% 80.0% | 92.5% 82.9% | No | |
Liu et al. [21] | FDG-PET | 93 | 146 | 100 | CNN and RNN | AD/CN MCI/CN | 91.2% 78.9% | 0.953 0.839 | Visualization of most contributing brain areas obtained from occlusion sensitivity analysis | |
Liu et al. [22] | MRI | 199 | – | 229 | CNN on landmarks selected by statistical significance tests | AD/CN MCIc/CN | 90.6% | 0.957 | Visualization of top 50 anatomical landmarks used as input for the CNN | |
Lin et al. [23] | MRI | 188 | 169/193 | 229 | CNN | AD/CN MCIc/MCInc | 88.8% 79.9% | 0.861 | No | |
Böhle et al. [14] | MRI | 211 | – | 169 | CNN | AD/CN | 88.0% | Visualization of LRP relevance and guided backpropagation maps, comparison of LRP relevance scores by group and brain region | ||
Li et al. [24] | MRI | Training 192 Test 225 | 383 479 | 228 639 | CNN for hippocampus only | AD/CN MCIc/MCInc | 92.9% | 0.958 0.891 | Visualization of most contributing hippocampus areas obtained from CNN class activation mapping | |
Dyrba et al. [11] | MRI | 189 | 219 | 254 | CNN for coronal slices covering hippocampus | AD/CN MCI/CN | 0.93 0.75 | Visualization of LRP and other methods’ relevance maps and comparison by diagnostic group | ||
Lian et al. [25] | MRI | Training 199 Test 159 | 167/226 38/239 | 229 200 | CNN | AD/CN MCIc/MCInc | 90.3% 80.9% | 0.951 0.781 | Visualization of most contributing image areas obtained from CNN class activation mapping | |
Qiu et al. [26] | MRI | Training 188 Test1 62 Test2 29 Test3 209 | – – – – | 229 320 73 356 | FCN | AD/CN1 AD/CN2 AD/CN3 | 87.0% 76.6% 81.8% | 0.870 0.892 0.881 | Visualization of most contributing brain areas obtained from occlusion sensitivity analysis | |
Wen et al. [27] | MRI | Training 336 Test1 76 Test2 78 | 295/298 20/13 – | 330 429 76 | CNN | AD/CN1 MCIc/MCInc1 AD/CN2 | 86% 50% 70% | No | ||
Thibeau-Sutre et al. [8] | MRI | Training 336 Test 76 | – – | 330 429 | CNN | AD/CN | 90% | Visualization of most contributing brain areas obtained from occlusion sensitivity analysis | ||
Jo et al. [28] | Tau-PET | 66 | – | 66 | CNN | AD/CN | 90.8% | Visualization of LRP relevance maps, visualization of most contributing brain areas obtained from occlusion sensitivity analysis | ||