Table 3.
Summary of MRI machine learning algorithms reviewed in (Rathore et al., 2017). p stands for converter MCI, s for stable MCI, CV indicates that cross-validation was used for evaluation.
| Study | Training sample size | Evaluation method | Follow-up time (years) | AUC |
|---|---|---|---|---|
| Misra et al. (2009) (Misra et al., 2009) | 27p/76 s | CV | Variable (Mean = 2 years) | 77% |
| Liu et al. (2013) (Liu et al., 2013) | 97p/93 s | CV | 3 | 72% |
| Eskildsen et al. (2013) (Eskildsen et al., 2013) | 128p/227 s | CV | Variable (Mean = 1.5 years) | 68% |
| Min et al. (2014) (Min et al., 2014) | 98AD/128NC | CV (117p/117 s) | Not reported | 67% |
| Liu et al. (2015) (Liu et al., 2015) | 117p/117 s | CV | Not reported | 81% |
| Tang et al. (2015) (Tang et al., 2015) | 175AD/210NC | CV (135p/87 s)) | 3 | 74% |
| Chincarini et al. (2011) (Chincarini et al., 2011) | 144AD/189NC | Independent set (136p/166 s) | 2 | 74% |
| Wee et al. (2013) (Wee et al., 2013) | 45p/56 s | Repeated hold-out (44p/55 s) | 3 | 84% |
| Sorensen et al. (2016) (Sorensen et al., 2016) | 101AD/169NC | Independent set (93p/140 s) | 2 | 74% |
| Hippocampus + Entorhinal Cortex | 230AD/230NC | Independent set (133p/198 s) | 2 | 73% |
| Hippocampus + Entorhinal Cortex | 230AD/230NC | Independent set (156p/152 s) | 3 | 76% |
| Hippocampus + Entorhinal Cortex | 230AD/230NC | Independent set (177p/84 s) | 5 | 84% |