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Table 1 Study population and characteristics

From: Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning

 

ADNI 1.5 T

ADNI 3 T

NACC

AIBL

Diagnosis

NC

MCI

AD

NC

MCI

AD

NC

AD

NC

AD

Number of cases

229

69

188

47

69

35

356

209

93

14

Age (median + range)

76 [60, 90]

76 [55, 88]

76 [55, 91]

75 [70–86]

76 [55, 88]

72 [57, 89]

74 [56, 94]

77 [55, 95]

71 [61, 86]

73 [58, 82]

Gender, male (percentage)

119 (51.96%)

39 (56.52%)

101 (53.72%)

18 (38.29%)

39 (56.52%)

12 (34.29%)

126 (35.39%)

95 (45.45%)

48 (51.61%)

6 (42.86%)

Education (median + range)

16 [6, 20]

16 [6, 20]

16 [4, 20]

16 [7, 20]

16 [6, 20]

14 [7, 20]

16 [0, 22]

14.5 [2, 24]

N.A.

N.A.

APOE+ (percentage)

61 (26.64%)

33 (47.83%)

124 (65.96%)

13 (27.66%)

33 (47.83%)

24 (68.75%)

102 (28.65%)

112 (53.59%)

1 (1.01%)

1 (7.17%)

MMSE (median + range)

29 [25, 30]

26 [24, 30]

23.5 [18, 28]

30 [26, 30]

26 [24, 30]

23 [20, 27]

29 [20, 30]

22 [0, 30]

29 [25, 30]

18 [6, 22]

  1. Three independent datasets including (a) the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, (b) the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL), and (c) the National Alzheimer’s Coordinating Center (NACC) were used for this study