Table 3 Performance comparison of our proposed multi-task, multi-stage deep transfer learning model using only connectome data versus only clinical data versus combined brain connectome and clinical data, for the joint prediction of abnormal cognitive, language, and motor outcomes at 2 years corrected age in very preterm infants.

From: A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants

Model

Cognition

BA (%)

Sen (%)

Spe (%)

LR + 

FPR (%)

AUC

Connectome data

70.9 ± 4.5

70.0 ± 6.9

71.7 ± 5.5

2.4 ± 0.4

28.3 ± 5.5

0.78 ± 0.06

Clinical data

69.1 ± 4.3

76.0 ± 4.6

62.1 ± 3.7

2.0 ± 0.2

37.9 ± 3.7

0.79 ± 0.04

Combined data

81.5 ± 3.2

74.0 ± 4.9

88.9 ± 3.1

6.6 ± 1.9

11.1 ± 3.1

0.86 ± 0.05

Model

Language

BA (%)

Sen (%)

Spe (%)

LR + 

FPR (%)

AUC

Connectome data

62.8 ± 3.7

63.2 ± 6.8

62.4 ± 5.7

1.6 ± 0.2

37.6 ± 5.7

0.60 ± 0.05

Clinical data

47.3 ± 3.2

41.0 ± 3.5

53.6 ± 4.1

0.9 ± 0.1

46.4 ± 4.1

0.51 ± 0.03

Combined data

68.9 ± 2.3

60.0 ± 4.0

77.8 ± 3.2

2.6 ± 0.3

22.2 ± 3.2

0.66 ± 0.03

Model

Motor

BA (%)

Sen (%)

Spe (%)

LR + 

FPR (%)

AUC

Connectome data

70.1 ± 4.2

72.0 ± 7.3

68.1 ± 5.3

2.2 ± 0.3

31.9 ± 5.3

0.66 ± 0.06

Clinical data

71.7 ± 3.3

74.0 ± 3.6

69.3 ± 3.6

2.4 ± 0.3

30.7 ± 3.6

0.72 ± 0.02

Combined data

73.9 ± 2.4

76.0 ± 4.9

71.7 ± 3.4

2.6 ± 0.2

28.3 ± 3.4

0.84 ± 0.02

  1. BA, balanced accuracy; Sen, sensitivity; Spe, specificity; LR+, likelihood ratio positive; FPR, false positive rate; AUC, area under the receiver operating characteristic curve.