Deep Learning for Accurate Bone Age Estimation: A Reliable VGG19-Based Methodology

Bone age assessment plays a critical role in diagnosing growth disorders and identifying developmental milestones, providing guidance for the timing of various clinical interventions. Traditional approaches are primarily subjective and heavily rely on visual evaluations. This study introduces a robust, accurate, and automated predictive model utilizing deep learning, built on a Kaggle dataset containing 12,611 annotated medical images. Stratified sampling was followed by splitting the data into balanced training (75%) and validation (25%) subsets. It had a pre-trained VGG19 model over ImageNet for feature extraction, after which a custom-defined set of dense layers was attached to it that would ultimately perform the detailed classification. The model was trained using Adam optimizer and tested by using the accuracy, precision, recall, F1 score and confusion matrices. The system also yielded validation accuracy of 94.77% with high precision and recall and these results were across the age groups. These outcomes support the model and attest to its viability for use in clinical setting. Thus, data augmentation and preprocessing methods were also used successfully to reduce overfitting and improve generality. The study has pointed out the transforming impact of AI in medical imagery, a strong tool for the automation of bone age assessment with excellent reliability. As a result, the applications of this AI-driven solution extend beyond clinical diagnostics to include forensic analysis and growth monitoring, showcasing its versatility across various healthcare domains. This thus helps develop efficiency and accuracy in evaluating skeletal maturity by addressing the limitations of traditional methods.