Enhancing pediatric distal radius fracture detection: optimizing YOLOv8 with advanced AI and machine learning techniques.
Journal:
BMC medical imaging
Published Date:
Aug 5, 2025
Abstract
BACKGROUND: In emergency departments, residents and physicians interpret X-rays to identify fractures, with distal radius fractures being the most common in children. Skilled radiologists typically ensure accurate readings in well-resourced hospitals, but rural areas often lack this expertise, leading to lower diagnostic accuracy and potential delays in treatment. Machine learning systems offer promising solutions by detecting subtle features that non-experts might miss. Recent advancements, including YOLOv8 and its attention-mechanism models, YOLOv8-AM, have shown potential in automated fracture detection. This study aims to refine the YOLOv8-AM model to improve the detection of distal radius fractures in pediatric patients by integrating targeted improvements and new attention mechanisms.
