results based on real clinical data demonstrate the feasibility and effectiveness of the proposed method. With the development and popularity of digital workflows, it is hoped that the work proposed in this paper can be more widely applied in dental clinical experiments in the future to facilitate doctors and patients in better carrying out their work. To overcome the problems and prospects raised in this paper, we will try to break through step by step in the future and constantly improve.
Background Existing state-of-the-art “safe zone” prediction methods are statistics-based methods, image-matching techniques, and machine learning methods. Yet, those methods bring a tension between accuracy and interpretability. Purpose To explore the model explanations and estimator consensus for “safe zone” prediction. Material and Methods We collected the pelvic datasets from Orthopaedic Hospital, and a novel acetabular cup detection method is proposed for automatic ROI segmentation. Hybrid priors comprising both specific priors from data and general priors from experts are constructed. Specifically, specific priors are constructed based on the fine-tuned ResNet-101 convolutional neural networks (CNN) model, and general priors are constructed based on expert knowledge. Our method considers the model explanations and dynamic consensus through appending a SHapley Additive exPlanations (SHAP) module and a dynamic estimator stacking. Results The proposed method achieves an accuracy of 99.40% and an area under the curve of 0.9998. Experimental results show that our model achieves superior results to the state-of-the-art conventional ensemble classifiers and deep CNN models. Conclusion This new screening model provides a new option for the “safe zone” prediction of acetabular cup.
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