Assessment of pre-trained deep learning models in the detection of metal artifacts in axial cone-beam tomography...

This study aimed to evaluate the performance of 3 pre-trained deep learning models (ResNet50, MobileNetV2, and EfficientNetB0) in the detection of metal artifacts (MAs) in axial cone-beam CT (CBCT) slices.

Two researchers calibrated, examined, and collected 1000 axial CBCT slices having crown and restoration-related artifacts (CRS) (n = 200), orthodontic bracket-related artifacts (OB) (n = 200), implant-related artifacts (IMP) (n = 200), root canal filling-related artifacts (RC) (n = 200) and 200 axial slices without artifacts (N) from a single CBCT unit. The image dataset was split at a 70:20:10 ratio for training, validation, and testing. Data augmentation was applied to the training data. Three pre-trained models (ResNet50, MobileNetV2, and EfficientNetB0) were trained, validated, and tested for the ability to classify data. The best-performing model was externally validated using axial images from different CBCT units from the same institution.

EfficientNetB0 was the best-performing model with 0.96 (95% CI, 0.93-0.98) test accuracy. Its precision, recall and F1 score were 0.98 (95% CI, 0.96-0.99), 0.96 (95% CI, 0.93-0.97), and 0.97 (95% CI, 0.95-0.99), respectively. EfficientNetB0 showed a significantly higher area under curve (AUC) value (0.98, 95% CI, 0.96-0.99) compared to other models. Its average classification time was 7.1 s. Gradient-weighted class activation mapping attention confirmed the focus of the model on MAs in the images. During external validation, EfficientNetB0 showed an accuracy of 0.93 (95% CI, 0.90-0.96) and an AUC-ROC of 0.95 (95% CI, 0.94-0.96). There was no significant difference in the performance metrics of EfficientNetB0 between test dataset and external validation dataset.

EfficientNetB0 demonstrated consistent and acceptable performance across all evaluation stages for the detection and classification of MAs in axial CBCT slices.

The study describes the performance of pre-trained model in the detection and classification of MAs. The model can be used by the clinician as a decision-support and quality-assessment tool in the CBCT imaging workflow.