Table 3.

Summary of studies using multi-modality imaging for AD prognosis at early stage (Acc.= Accuracy; Sens.=Sensitivity; Spec.=Specificity)

Papers	Stage	Imaging modalities	Non-imaging data	Cross-sectional or longitudinal	ML model	Data source	Sample size	Acc.	Sens.	Spec.
Ritter et al. (16)	MCI	MRI, FDG-PET	CSF, neuropsychological testing, medical history, symptoms, neurological & physical exams, demographics	cross-sectional	SVM	ADNI	86 MCI-c vs. 151 MCI-nc	0.730	0.405	0.913
Shaffer et al. (17)	MCI	MRI, FDG-PET	CSF, neuropsychological testing, APOE, age, education	cross-sectional	ICA and logistic regression	ADNI	43 MCI-c vs. 54 MCI-nc	0.716	0.853	0.862
Zhang et al. (18)	MCI	MRI, FDG-PET	CSF	cross-sectional	MKL	ADNI	51 AD, 43 MCI-c, 56 MCI-nc, 52 NC	0.764	0.818	0.660
Liu et al. (19)	MCI	MRI, FDG-PET	none	cross-sectional	LASSO & MKL	ADNI	44 MCI-c vs. 56 MCI-nc	0.678	0.649	0.700
Zhang and Shen (20)	MCI	MRI, FDG-PET	CSF	cross-sectional	Multi-task learning & SVM	ADNI	40 AD, 38 MCI-c, 42 MCI-nc, 47 NC	0.739	0.686	0.736
Cheng et al. (21)	MCI	MRI, FDG-PET	CSF	cross-sectional	Transfer learning & group lasso	ADNI	51 AD, 43 MCI-c, 56 MCI-nc, 52 NC	0.794	0.845	0.727
Young et al. (22)	MCI	MRI, FDG-PET	CSF, APOE	cross-sectional	GP for AD vs. NC, applied to MCI	ADNI	47 MCI-c vs. 96 MCI-nc	0.741	0.787	0.656
Hinrichs et al. (23)	MCI	MRI, FDG-PET	CSF, APOE, NeuroPshychological Status Exam scores	Longitudinal	MKL for AD vs. NC, applied to MCI	ADNI	48 AD, 119 MCI, 66NC	0.791	N/A	N/A
Zhang et al. (24)	MCI	MRI, FDG-PET	CSF, MMSE, ADAS-Cog	Longitudinal	MKL	ADNI	35 MCI-c vs. 50 MCI-nc	0.784	0.79	0.780
Wang et al. (25)	MCI	MRI, FDG-PET, flobetapir-PET	ADAS-Cog	cross-sectional	PLS	ADNI	64 MCI-c vs. 65 MCI-nc	0.861	0.813	0.907