Tarek Barhoum, Raghad Nahhas and Ahmad Nassar

Researchers

Post Graduate Studies & Research Council Meeting No. 4, 11/05/2025

Date of Acceptance

Bone age assessment is a crucial tool for diagnosing abnormal growth conditions such as precocious puberty. Traditional methods rely on manual visual assessment, making them prone to human errors. Deep learning, particularly Convolutional Neural Networks (CNNs), offers an efficient and precise alternative by analyzing X-ray images of bones and identifying maturation patterns. This research aims to develop an AI-based model that can automatically estimate bone age with high accuracy, assisting doctors in early diagnosis and improving pediatric healthcare.

Abstract