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Fractures: Bone Repair

Fractures: Bone Repair

Treatment for a fracture is based on the type of break, the bone affected, and the patient's age.
Minor fractures with no bone displacement are treated by immobilizing the fractured bone using a cast or splint. However, in the case of fractures with displaced bones, the broken bones are repositioned before immobilization to ensure successful healing without deformation and loss of function. The realignment of fractured bone ends is performed through a process called reduction. If the...

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An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

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Artificial intelligence-based method for detecting wrist fractures in children.

Dongren Liu^1,2, Zhiyuan Yang³, Chunyu Bao⁴

¹School of Sports Health, Tianjin University of Sport, Tianjin, China.

Scientific Reports

|November 4, 2025

Summary

This study introduces Kid-YOLO, an AI tool for detecting pediatric wrist fractures in X-rays. The improved deep learning model enhances diagnostic accuracy and efficiency, aiding physicians in identifying complex fractures.

Keywords:

Artificial intelligence C3k2-WTConv Focaler-MPDIoU Pediatric wrist fractures Radiology YOLO11

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Area of Science:

Medical Imaging
Artificial Intelligence
Orthopedics

Background:

Pediatric wrist fractures are common but challenging to diagnose due to developing bone structures.
Traditional diagnosis relies on physician experience, risking misdiagnosis and inefficiency, especially in resource-limited settings.

Purpose of the Study:

To develop an improved deep learning detection method, Kid-YOLO, for accurate and efficient automatic detection of pediatric wrist fractures in X-ray images.
To enhance fracture detection by optimizing feature extraction, localization accuracy, and addressing class imbalance issues.

Main Methods:

An improved deep learning model, Kid-YOLO, based on YOLO11s, incorporating the C3k2-WTConv module and Focaler-MPDIoU loss function.
Wavelet transform and convolution operations were combined in the C3k2-WTConv module for enhanced feature extraction.
The Focaler-MPDIoU loss function was utilized to improve detection of rare targets and optimize localization.

Main Results:

The Kid-YOLO model demonstrated a 3.2% increase in precision, 1.6% in recall, 1.8% in mAP@50, and 3.2% in mAP@50-95 compared to the baseline YOLO11 model.
The AI-assisted diagnostic system provided efficient image loading, fracture detection, and result visualization.
The system offers a reliable tool for physicians, improving diagnostic capabilities for pediatric wrist fractures.

Conclusions:

The proposed Kid-YOLO model significantly improves the accuracy and efficiency of pediatric wrist fracture detection in X-rays.
The developed AI system serves as a valuable tool for clinical practice, supporting physicians in diagnosing complex fractures.
This deep learning approach holds potential for broader applications in medical imaging analysis and precision medicine.