Abstract
Skeletal bone age assessment is a common clinical practice to analyze and assess the biological maturity of pediatric patients. This process generally involves taking X-ray of the left hand, along with fingers and wrist, then followed by image analysis. The current process involves manually comparing the radiological scan of this X-ray with the standard reference images and estimating the skeletal age. The analysis is crucial in determining if a child is prone to some disease. This current manual process is very time consuming and has high probabilities of misjudgment in predicting the skeletal age. However, recent developments in the field of neural networks provide an opportunity to automate this process. In this project, we are using convolutional neural network methods and image processing techniques to fully automate the process of predicting the skeletal bone age of a patient from the given X-ray images. Radiological Society of North America has collected a dataset with 12600 different hand images of boys and girls from Colorado children’s hospital and Stanford children’s hospital and provided for research purposes. This dataset is used to train and build a convolutional neural network model in this study. Each image in the dataset is a complete image of a left-hand wrist and a CSV file containing the corresponding age in months and gender. The purpose of this project is to automate the current manual process and develop a tool that helps doctors and act as a decision support system in predicting the skeletal age. Along with this, we are developing a user-friendly and highly available online system which helps the doctors in predicting the bone age accurately.