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

Objectives This study aimed to evaluate the performance of three pre-trained deep learning models (ResNet50, MobileNetV2, and EfficientNetB0) in the detection of metal artefacts (MAs) in axial cone-beam computed tomography (CBCT) slices. Methods Two researchers calibrated, examined and collected 1000 axial CBCT slices having crown and restoration-related MAs (CRS) (n = 200), orthodontic bracket-related MAs (OB) (n = 200), implant plant–related MAs (IMP) (n = 200), Root canal filling-related MAs (RC) (n = 200) and 200 axial slices without artefacts (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. Results 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 data set and external validation dataset. Conclusions EfficientNetB0 demonstrated consistent and acceptable performance across all evaluation stages for the detection and classification of MAs in axial CBCT slices. Advances in knowledge 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.