Table 4 Application of machine learning based on imaging biomarker genomics in AD diagnosis and prognosis
From: A review of brain imaging biomarker genomics in Alzheimer’s disease: implementation and perspectives
Method | Year | Modality | Model | Dataset | CV | Neural location | Results |
|---|---|---|---|---|---|---|---|
Machine learning | 2010 [156] | sMRI, FDG PET, CSF, APOE genotype, age, sex, body mass index | SVM | HC: 213 AD: 158 MCI: 264 | LOOCV | Hippocampal, ventricular, temporal lobe | A maximum up to 90% accuracy for AD |
2013 [155] | sMRI, FDG PET, CSF, APOE genotype | MRF | HC: 35 AD: 37 MCI: 75 | Fourfold CV | Whole brain | An accuracy of 89% for AD | |
2014 [164] | sMRI, FDG PET, CSF, SNP | SVM | HC: 47 AD: 49 MCI: 93 | Tenfold CV | Whole brain | An accuracy of 71% among HC, MCI and AD | |
2016 [157] | APOE genotype, neuropsychological assessment, sMRI, FDG PET | NB | HC: 112 AD: 144 sMCI: 265 pMCI: 177 | independent test set | Whole brain | An accuracy of 87% in identifying pMCI from sMCI | |
2017 [159] | sMRI, SNP | HYDRA | HC: 139 AD: 103 | – | Hippocampus, entorhinal cortex frontal lobe | The highest AUC value of 0.942 for AD | |
2017 [165] | sMRI, SNP | SVM | HC: 204 AD: 171 MCI: 362 | Tenfold CV | Whole brain | An accuracy of 80.8% identifying pMCI from sMCI | |
2019 [158] | fMRI, SNP | MRF | HC: 35 AD: 37 | – | Olfactory cortex, insula, posterior cingulate gyrus and lingual gyrus | An accuracy of 87% AD prediction | |
2019 [154] | SNP | LASSO, KNN, SVM | HC: 371 AD: 267 | CV | – | The highest reached 0.72 of the AUC | |
2019 [166] | APOE, PET, PGS | LR | HC: 224 AD: 174 MCI: 344 | – | Whole brain | An AUC value of 0.69 using PGS and APOE to predict amyloid state | |
2020 [167] | sMRI, FDG PET, AV45 PET, DTI, resting-state fMRI, APOE genotype | MKL | HC: 35 AD: 33 sMCI: 30 pMCI: 31 | LOOCV | Whole brain | An accuracy of 96.9% in identifying pMCI from sMCI | |
Deep learning | 2017 [162] | SNP, sMRI FDG PET | DFFF | HC: 226 AD: 190 MCI: 389 | Twentyfold CV | Whole brain | An accuracy of 0.65 among HC, MCI and AD |
2018 [68] | sMRI, SNP | NN | HC: 225 AD: 138 MCI: 358 | Fivefold CV | 16 ROIs (hippocampus, entorhinal cortex, parahippocampal gyrus, amygdala, precuneus, etc.) | An AUC value of 0.992 using combined features | |
2019 [161] | sMRI, demographic, neuropsychological assessment, APOE genotype data | CNN | HC: 184 AD: 192 sMCI: 228 pMCI: 181 | Tenfold CV | Whole brain | An AUC value of 0.925 for pMCI prediction | |
2019 [160] | DTI, SNP | DCNN | HC: 100 AD: 51 | Fivefold CV | Temporal lobes (including the hippocampus) and the ventricular system | The highest AUC value of 0.858 | |
2021 [61] | MRI, SNP, electronic health records | CNN | ADNI | independent test set | Whole brain | A maximum up to 87% accuracy |