Abstract Background Bone age assessment is a critical tool in pediatric orthopedic surgery, guiding treatment decisions for growth-related disorders and surgical interventions. Traditional methods, such as the Greulich-Pyle and Tanner-Whitehouse techniques, rely on manual interpretation of hand and wrist radiographs, making them time-intensive and susceptible to inter-operator variability. Artificial intelligence (AI) has emerged as a promising tool to enhance accuracy, efficiency, and standardization in skeletal maturity assessment. Methods This study evaluates the application of AI in pediatric bone age prediction using the Radiological Society of North America (RSNA) 2017 Pediatric Bone Age Challenge dataset. A deep learning model based on the ResNet-50 architecture (Microsoft Research, Redmond, Washington, USA) was developed and trained on 12,611 hand and wrist radiographs, validated on 1,425 images, and tested on 200 images. Model performance […]
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