Improving pediatric trauma care: an automated system for wrist trauma detection using GELAN

Abstract Trauma is a major cause of disability among children, requiring swift and accurate diagnosis for effective treatment. This paper introduces an automated method that uses deep learning to detect and categorize fractures in children using X-ray images. The system makes use of the GRAZPEDWRI-DX dataset, which consists of 20,327 annotated X-ray images of pediatric wrist fractures. Our architecture, which is built upon the generalized efficient layer aggregation network (GELAN), effectively tackles the issues of class imbalance and image resolution. As a result, it achieves state-of-the-art performance in both trauma and severity detection. Our proposed framework surpassed the most advanced techniques, showcasing exceptional precision and effectiveness, achieving a mean average precision (mAP50) score of 74.1%, 95%, and 85.5% for Task A (trauma detection), Task B (fracture detection), and Task C (fracture severity detection), respectively. The results of our study highlight the capacity of deep learning to improve the diagnosis of pediatric trauma, decrease the burden on radiologists, and boost patient outcomes.