Table 2.

A brief description of the datasets used for the validation of cortical thickness-based AD classification frameworks

Study	Subjects					Type	Classification algorithm	Database	Classification accuracy
	AD	MCI	pMCI	sMCI	CN				AD/CN	AD/MCI	MCI/CN	sMCI/pMCI
(Li et al., 2014b)	—	24	—	—	26	VAF	SVM	XWH	—	—	80.00	—
(Cho et al., 2012)	128	—	72	131	160	RV	LDA	ADNI	88.33	—	—	71.21
(Park et al., 2012)	25	25	—	—	50	RV	SVM	OASIS	90.00	90.00	86.00	—
(Park et al., 2013)	—	30	12	—	30	RV	SVM	ADNI	—	83.001	90.00	—
(Desikan et al., 2009)	65	57	—	—	94	Atlas	LR	ADNI	95.00	—	95.00	—
(McEvoy et al., 2009)	84	175	—	—	139	Atlas	LDA	ADNI	89.00	—	—	—
(Oliveira et al., 2010)	14	—	—	—	20	Atlas	SVM	DPUSP	88.20	—	—	—
(Eskildsen et al., 2013)	194	—	340	134	226	Atlas	LDA	ADNI	86.70	—	—	71.10
(Eskildsen et al., 2013)	—	—	29 (36)	134	–	Atlas	LDA	ADNI	—	—	—	77.30
(Eskildsen et al., 2013)	—	—	61 (24)	134	—	Atlas	LDA	ADNI	—	—	—	73.00
(Eskildsen et al., 2013)	—	—	128 (12)	134	—	Atlas	LDA	ADNI	—	—	—	74.50
(Eskildsen et al., 2013)	—	—	122 (06)	134	—	Atlas	LDA	ADNI	—	—	—	80.90
(Wee et al., 2013)	198	—	89	111	200	Atlas	Multi-kernel SVM	ADNI	92.35	79.24	83.75	75.05
(Lillemark et al., 2014)	114	240	—	—	170	Atlas	LDA	ADNI	87.70*	76.60*	78.40*	—

RV = Reduced vertex-wise

XWH = Xuan Wu Hospital, Beijing, China

D Department of s chiatr, niversit of ao aulo, ao aulo, razil

¹= prediction of conversion from MCI to AD

^*shows AUC.

The first entry for (Eskildsen et al., 2013) shows the classification accuracy by using all the sMCIs and pMCIs. The remaining entries show the classification between sMCI and different sets of pMCI subjects, divided according to their time of conversion from sMCI to pMCI. Numbers of months are shown in parenthesis against pMCI subjects.