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Table 2 Performance of individual DLMs for body part classification: comparison among the algorithm models based on preprocessing methods and planes

From: Development and validation of an ensemble artificial intelligence model for comprehensive imaging quality check to classify body parts and contrast enhancement

Pre-processing Method

Internal validation set

External validation set

Precision (%)

Recall (%)

Accuracy (%)

F1-score (%)

Precision (%)

Recall (%)

Accuracy (%)

F1-score (%)

AIP

        

Axial

100

100

100

100

94.99

94.15

94.86

94.6

Sagittal

100

100

100

100

97.52

96.85

97.09

97.2

Coronal

100

100

100

100

95.24

91.73

93.75

93.45

MIP

        

Axial

100

100

100

100

98.85

98.33

98.66a

98.6

Sagittal

100

100

100

100

99.33

99.33

99.33ab

99.3

Coronal

100

100

100

100

98.85

98.33

98.66a

98.6

Mid-plane

        

Axial

100

100

100

100

96.24

96.51

96.2

96.4

Sagittal

100

100

100

100

97.7

96.32

97.32

97

Coronal

100

100

100

100

96.24

96.51

96.2

96.3

  1. aThe DLMs with the highest performance in each plane are selected to apply for ensemble AI model
  2. bThe DLM with the highest performance from all pre-processing methods is selected as the best performing individual DLM