Background <p>An accurate preoperative assessment of alveolar bone morphology is essential to the success of dental implant surgery. Although Cone Beam Computed Tomography (CBCT) provides high-resolution volumetric imaging for this purpose, interpreting it requires substantial expertise and remains inherently subjective. Integrating artificial intelligence (AI), specifically three-dimensional convolutional neural networks (3D-CNNs), could automate and standardize CBCT interpretation. However, there has been no systematic development of AI-driven preoperative tools capable of objectively predicting the necessity of adjunctive procedures, such as guided bone regeneration or maxillary sinus elevation.</p> Methods <p>We retrospectively collect 285 CBCT datasets from a single institution from patients undergoing dental implant surgery. Then, we construct a 3D-CNN–based deep learning model to automatically predict the need for adjunctive surgical intervention prior to implant placement. To optimize the model’s performance, we design a four-stage optimization framework that incorporates multimodal data augmentation, learning rate decay scheduling, optimizer selection, and convolutional channel configuration. We comprehensively evaluate model performance using accuracy (ACC), area under the receiver operating characteristic curve (AUC), and F1-score across training, validation, and test sets. We employ Grad-CAM visualization to reveal spatial attention patterns and apply LASSO regression to extract key latent features from the model’s fully connected layers. These features are then used to create a quantitative nomogram to improve clinical interpretability.</p> Results <p>The optimized 3D-CNN achieves an accuracy of 0.81, an AUC of 0.79, and an F1-score of 0.82 on the validation and test sets, demonstrating strong discriminative and generalizability capabilities. Grad-CAM heatmaps shows that the model focuses on the edentulous ridge and the adjacent maxillary sinus regions, which are areas that align with expert clinical reasoning. LASSO regression identifies 14 high-contribution features for constructing an interpretable clinical nomogram (AUC = 0.855). Decision curve analysis indicates a positive net clinical benefit across multiple threshold ranges.</p> Conclusions <p>This study presents a 3D-CNN-based CBCT interpretation model that can objectively predict the need for bone augmentation procedures before implant surgery. Integrating multimodal data augmentation and standardized Hounsfield unit (HU) normalization significantly improve the model’s robustness and generalization. By combining deep learning with clinical decision-making processes, this study provides an interpretable, quantitative artificial intelligence (AI) framework for preoperative implant assessment. As the current model was developed, optimized, and tested on a single-center dataset, prospective multicenter external validation across diverse patient populations, varied CBCT acquisition protocols, and different clinical practice settings is essential to establish its generalizability and clinical utility before widespread deployment. This framework offers a feasible paradigm for future intelligent, standardized surgical planning in dental implantology.</p>