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Updated: Mar 12, 2026

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
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A Benchmark X-ray Dataset for Pediatric Supracondylar Humerus Fractures with Improved YOLOv11-Based Detection

Zhu Xiong1,2, Kaize Zheng2, Huating Chen2

  • 1School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, 999078, Macau, China.

Scientific Data
|March 11, 2026
PubMed

Insights

This study introduces PediaSHF-DX, a large dataset of pediatric elbow X-rays, and an AI model for diagnosing supracondylar humerus fractures (SHFs) in children, improving diagnostic accuracy.

Area of Science:

  • Orthopedic Surgery
  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis

Background:

  • Supracondylar humerus fractures (SHFs) are the most common pediatric elbow fractures, requiring precise diagnosis to prevent complications.
  • Current diagnostic methods for pediatric SHFs can be challenging, necessitating advanced tools for accuracy and timeliness.

Purpose of the Study:

  • To introduce PediaSHF-DX, a comprehensive dataset of pediatric elbow X-rays for AI development.
  • To develop and evaluate an AI model for automated and accurate detection of pediatric SHFs.

Main Methods:

  • Curated PediaSHF-DX dataset: 10,325 de-identified pediatric elbow X-rays (5,163 patients), with 2,015 images expertly annotated.
  • Developed an improved YOLOv11-based detection model with LocalAttention and optimized structure for enhanced fracture detection.
  • Validated the model on a separate test set of 8,310 images.

Main Results:

  • The AI model achieved a precision of 0.96 on the test dataset.
  • The model demonstrated high performance, strong generalization, and robustness across diverse imaging conditions.
  • PediaSHF-DX dataset is publicly available for research.

Conclusions:

  • PediaSHF-DX dataset and the proposed AI model offer a valuable resource for advancing AI-driven diagnosis of pediatric SHFs.
  • This work supports the development of AI tools to aid orthopedic surgeons in pediatric fracture care.
  • The findings highlight the potential of AI in improving the accuracy and efficiency of diagnosing common pediatric fractures.

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