Skip to main content

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