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MaxI-Net: A 3D AI Framework for CBCT-Based Maxillofacial Defect Reconstruction and Patient-Specific Implant
Mamta Juneja1, Maanya Kharbanda1, Nitin Pandey1
1University Institute of Engineering and Technology, Panjab University, Chandigarh 160014, India.
Bioengineering (Basel, Switzerland)
|June 26, 2026
Summary
Maxillofacial Implant-generation Network (MaxI-Net) offers fast, efficient 3D deep learning for maxillofacial defect reconstruction and patient-specific implant generation. This advanced framework significantly improves accuracy and reduces complexity in reconstructive surgery.
Area of Science:
- Biomedical Engineering
- Medical Imaging
- Artificial Intelligence
Background:
- Maxillofacial defects present significant challenges in aesthetics and function.
- Current reconstruction methods using CAD and grafts are complex, time-consuming, and involve donor-site morbidity.
- Existing deep learning models for reconstruction are often task-specific, lack multi-scale features, and use limited datasets.
Purpose of the Study:
- To introduce the Maxillofacial Implant-generation Network (MaxI-Net), a novel 3D deep learning framework.
- To achieve fast, resource-efficient, end-to-end reconstruction of maxillofacial defects and generation of patient-specific implants.
- To integrate multi-scale feature learning and address limitations of prior deep learning approaches.
Main Methods:
- Developed a 3D encoder-bottleneck-decoder architecture with hybrid dilated convolutions, residual connections, SE blocks, and 3D CBAMs.
- Trained MaxI-Net on 921 Cone Beam-Computed Tomography (CBCT) scans, augmented to 11,973 pairs, using Dice loss and Adam optimization.
- Benchmarked against UNet, UNETR, SegResNet, and SwinUNETR, with statistical validation using Wilcoxon signed-rank tests.
Main Results:
- MaxI-Net demonstrated superior performance with a Dice Similarity Coefficient (DSC) of 0.778 and Hausdorff Distance (HD95) of 3.453 mm.
- Achieved statistically significant DSC improvements (p < 0.001) over all competing architectures.
- Exhibited high efficiency with a processing time of 0.06 s/volume and 9.6 min/epoch.
Conclusions:
- MaxI-Net provides a fast, efficient, and accurate solution for maxillofacial defect reconstruction and patient-specific implant generation.
- The framework's advanced architecture effectively integrates multi-scale features for improved performance.
- Biomechanical validation indicated significant stress reduction in generated PEEK implants, predicting a long functional lifespan.
