Bone Age Calculation Prediction

Bone age assessment is vital in pediatric healthcare for evaluating growth and maturity. This paper presents an automated bone age prediction system using Convolutional Neural Networks (CNNs) combined with linear regression. Left-hand radiographs were pre-processed and analysed using CNNs to extract features, followed by regression for prediction. The model achieved a low Mean Absolute Error (MAE), outperforming traditional methods. Results demonstrate deep learning’s potential to deliver fast, accurate, and reproducible bone age estimates, aiding clinical decision-making in pediatric care.