Table 1.
Brief review of methods employed by recent studies that have used machine learning and statistical learning techniques to predict PD from MRI modalities.
| Author, year | Number of subjects | Methods employed | Accuracy (%) |
|---|---|---|---|
| Salvatore et al., 2014 | PD (n = 28) | VBM | PD vs HC: 83.2 |
| PSP (n = 28) | Principal component analysis | PSP vs HC: 86.2 | |
| HC (n = 28) | SVM | PSP vs PD: 84.7 | |
| Cherubini et al., 2014a, Cherubini et al., 2014b | Tremor dominant PD (n = 15) | VBM, DTI | |
| ET with rest tremor (n = 15) | SVM | ||
| Cherubini et al., 2014a, Cherubini et al., 2014b | PD (n = 57) | VBM, DTI | 100 |
| PSP (n = 21) | SVM | ||
| Rana et al., 2015 | PD (n = 30) | Region of interest based | 86.67 |
| HC (n = 30) | SVM | ||
| Singh and Samavedham, 2015 | PPMI cohort | Self-organizing maps | 99.9 |
| PD (n = 518) | SVM | ||
| SWEDD (n = 68) | |||
| HC (n = 245) | |||
| Huppertz et al., 2016 | PD (n = 204) | Volumetry | 80 |
| PSP-RS (n = 106) | SVM | ||
| MSA-C (n = 21) | |||
| MSA-P (n = 60) | |||
| Adeli et al., 2016 | PPMI cohort | Joint feature-sample selection | 81.9 |
| PD (n = 374) | |||
| HC (n = 169) | |||
| Abos et al., 2017 | PD (n = 27) | Functional connectome | 80 |
| HC (n = 38) | SVM | ||
| Peran et al.,2018 | PD (n = 26) | VBM, T2* relaxometry, DTI | PD vs MSA: 96 |
| MSA-P (n = 16) | |||
| MSA-C (n = 13) | Self-organizing maps | ||
| HC (n = 26) | |||
| Amoroso et al., 2018 | PPMI cohort | Connectivity measures | 93 |
| PD (n = 374) | SVM | ||
| HC (n = 169) | |||
| Ariz et al., 2019 | PD (n = 40) | NM-MRI based atlas of | 79.9 |
| HC (n = 39) | Substantia nigra |
DTI: Diffusion tensor imaging; ET: Essential tremor; HC: Healthy controls: MSA-C: Multiple system atrophy with predominant cerebellar features; MSA-P: Multiple system atrophy with predominant parkinsonian features; NM-MRI: Neuromelanin sensitive magnetic resonance imaging; PD: Parkinson's disease; PPMI:Parkinson's Progression Markers Initiative; PSP: Progressive supranuclear palsy; PSP-RS: Progressive supranuclear palsy-Richardson syndrome; SVM: Support vector machine; SWEDD: Scans without evidence of dopaminergic deficit; VBM: Voxel based morphometry.