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IDNet: A Diffusion Model-Enhanced Framework for Accurate Cranio-Maxillofacial Bone Defect Repair.

Xueqin Ji^1,2, Wensheng Wang^3,4, Xiaobiao Zhang^4,5

¹The Third School of Clinical Medicine, Ningxia Medical University, Yinchuan 750000, China.

Bioengineering (Basel, Switzerland)

|April 26, 2025

Summary

A novel Internal Diffusion Network (IDNet) improves cranio-maxillofacial bone defect repair. This AI approach enhances 3D segmentation accuracy, outperforming existing methods for better surgical outcomes.

Keywords:

3D reconstruction bone defect repair cranio-maxillofacial surgery deep learning diffusion model medical image segmentation

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Area of Science:

Oral and Maxillofacial Surgery
Medical Imaging
Artificial Intelligence

Background:

Cranio-maxillofacial bone defect repair is complex, impacting function and aesthetics.
Traditional reconstruction methods have limitations for intricate defects.
Advanced computational approaches are needed for precise, personalized reconstruction.

Purpose of the Study:

To introduce the Internal Diffusion Network (IDNet) for high-precision 3D medical segmentation.
To evaluate IDNet's performance in cranio-maxillofacial bone defect repair.
To demonstrate the advantages of diffusion model-based approaches in this surgical field.

Main Methods:

Developed IDNet, integrating a diffusion model with a U-Net architecture.
Incorporated a Step-Uncertainty Fusion module for enhanced prediction robustness.
Evaluated IDNet on 125 normal skulls and 2625 simulated defects.

Main Results:

IDNet significantly outperformed UNETR and 3D U-Net in quantitative evaluations.
Achieved an average Dice Similarity Coefficient (DSC) of 0.8140.
Achieved an average True Positive Rate (RECALL) of 0.8554 and HD95 of 4.35 mm.

Conclusions:

IDNet demonstrates superior performance for cranio-maxillofacial bone defect segmentation.
Diffusion model-based methods show significant advantages in this application.
IDNet has potential to improve surgical success rates and patient satisfaction.