Bone Age Estimation of Pediatrics by Analyzing Hand X-Rays Using Deep Learning Technique

The determination of bone age is critical for detecting metabolic and endocrine problems in a child's development. It provides important insights on the rate of structural and biological development, which frequently differs compared to the chronological age determined at birth. This study presents a completely automated deep learning technique for correctly determining bone age from X-ray images of the hand. The dataset used for training and evaluation is derived from the Radiological Society of North America's 2017 Pediatric Bone Age Challenge, which comprises left hand X-ray pictures annotated with gender and age information. To determine bone age precisely, we use a transfer learning technique using the pre-trained Xception model. By fine-tuning the neural network on the bone age dataset, it was possible to capture complicated patterns and attributes peculiar to bone development. With a mean absolute error (MAE) of 1.52 months, the experimental results show a remarkable level of convergence between the predicted age and the actual chronological age. The therapeutic significance of our suggested technique arises from its potential to be a beneficial tool to assist medical practitioners in more correctly and effectively determining bone age. The incorporation of AI-based autonomous bone age assessment can help reduce the diagnostic process and assist in early identification of developmental anomalies, ultimately contributing to improved pediatric healthcare.