AI-Driven CBCT Analysis for Surgical Decision-Making and Mucosal Damage Prediction in Sinus Lift Surgery for patients with low RBH.

Background: Decision-making for maxillary sinus floor elevation (MSFE) surgery in patients with low residual bone height (<4 mm) presents significant challenges, particularly in selecting surgical approaches and predicting intraoperative mucosal perforation. Traditional methods rely heavily on physician experience, lack standardization and objectivity, and often fail to meet the demands of precision medicine. This study aims to build an intelligent decision-making system based on deep learning to optimize surgical selection and predict the risk of mucosal perforation, providing clinicians with a reliable auxiliary tool.

Methods: This study retrospectively analysed the cone-beam computed tomography imaging data of 79 patients who underwent MSFE and constructed a three-dimensional (3D) deep-learning model based on the overall CT data of the patients for surgical procedure selection and prediction of mucosal perforation. The model innovatively introduced the Convolutional Block Attention Module mechanism and depthwise separable convolution technology to enhance the model's ability to capture spatial features and computational efficiency. The model was rigorously trained and validated on multiple datasets, with visualization achieved through attention heatmaps to improve interpretability.

Results: The modified EfficientNet model achieved an F1 score of 0.6 in the procedure decision task of MSFE. For predicting mucosal perforation, the improved ResNet model achieved an accuracy of 0.8485 and an F1-score of 0.7273 on the mixed dataset. In the experimental group, the improved ResNet model achieved an accuracy of 0.8235, a recall of 0.7619, and an F1-score of 0.7302. In the control group, the model also maintained stable performance, with an F1-score of 0.6483. Overall, the 3D convolutional model enhanced the accuracy and stability of mucosal perforation prediction by leveraging the spatial features of cone-beam computed tomography imaging, demonstrating a certain degree of generalization capability.

Conclusion: This study is the first to construct a deep learning-based 3D intelligent decision-making model for MSFE. These findings confirm the model's effectiveness in surgical decision-making and in predicting the risk of mucosal perforation. The system provides an objective decision-making basis for clinicians, improves the standardization level of complex case management, and demonstrates potential for clinical application.