An AI-Driven Framework for Detecting Bone Fractures in Orthopedic Therapy.

Area of Science:

• Medical Imaging
• Artificial Intelligence in Healthcare
• Orthopedic Diagnostics

Background:

• Traditional bone fracture detection relies on manual analysis of X-ray images, which can be time-consuming and prone to errors.
• Limitations in current diagnostic methods necessitate the development of automated tools to improve efficiency and accuracy.

Purpose of the Study:

• To develop and evaluate an advanced artificial intelligence (AI)-driven framework for enhanced speed and accuracy in bone fracture detection.
• To address the limitations of manual image analysis in orthopedic diagnostics.

Main Methods:

• Integration of the YOLOv8 object detection model with a ResNet backbone for robust feature extraction and precise fracture classification.
• Utilizing a comprehensive dataset of X-ray images for model training and validation.

Main Results:

• The AI framework achieved a mean average precision of 0.9.
• The model demonstrated an overall classification accuracy of 90.5% for bone fracture detection.
• Significant improvements in diagnostic performance compared to conventional methods were observed.

Conclusions:

• The proposed AI framework offers a powerful, automated solution for orthopedic diagnostics.
• This technology has the potential to enhance diagnostic efficiency and accuracy in both routine and emergency care settings.
• The study contributes to improved patient outcomes through timely and accurate fracture intervention.