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Scaphoid Fracture Detection and Localization Using Denoising Diffusion Models.

Zhih-Cheng Huang¹, Tai-Hua Yang^2,3, Zhen-Li Yang¹

¹Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan.

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|January 10, 2026

Summary

This summary is machine-generated.

This study introduces a deep learning system using diffusion models for faster and more accurate scaphoid fracture detection and localization. The AI model significantly improves diagnostic speed and precision, identifying subtle fractures often missed by traditional X-rays.

Keywords:

anomaly detection diffusion models scaphoid fracture detection and localization self-supervised

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Area of Science:

Medical Imaging
Artificial Intelligence in Radiology
Orthopedic Diagnostics

Background:

Scaphoid fractures are common wrist injuries requiring timely diagnosis.
Occult and nondisplaced scaphoid fractures present diagnostic challenges with conventional X-rays.
Accurate fracture detection and localization are crucial for effective treatment.

Purpose of the Study:

To develop a deep learning-based system for scaphoid fracture detection and localization.
To leverage diffusion models for enhanced diagnostic accuracy and efficiency.
To create a reliable AI-assisted tool to aid radiologists in identifying subtle fractures.

Main Methods:

A three-stage framework utilizing diffusion models for scaphoid fracture analysis.
Stage I: Self-supervised data augmentation by embedding pseudofractures into healthy scaphoid images.
Stage II & III: Diffusion-based reconstruction of pseudofractured images and a U-Net-like network to identify fracture-induced differences.

Main Results:

High diagnostic performance for scaphoid fracture detection with an image AUROC of 0.993, accuracy of 0.983, recall of 1.00, and precision of 0.975.
Precise fracture localization achieved with a pixel AUROC of 0.978 and pixel region overlap of 0.921.
The model demonstrated superior performance compared to traditional diagnostic methods.

Conclusions:

The proposed diffusion model framework offers a reliable, powerful, and scalable solution for scaphoid fracture detection and localization.
Denoising diffusion models significantly reduce diagnostic time and improve the identification of subtle fractures.
This AI-driven approach has the potential to enhance clinical workflows and patient outcomes in orthopedic diagnostics.