Deep Learning Framework for Automatic Bone Age Assessment

Bone age Assessment or the skeletal age is a general clinical practice to detect endocrine and metabolic disarrangement in child development. The bone age indicates the level of structural and biological growth better than chronological age calculated from the birth date. The X-Ray of the wrist and hand is used in common to estimate the bone age of a person. The degree of agreement among the automated methods used to evaluate the X-rays is more than any other manual method. In this work, we propose a fully automated deep learning approach for bone age assessment. The dataset used is from the 2017 Pediatric Bone Age Challenge released by the Radiological Society of North America. Each X-Ray image in this dataset is an image of a left hand tagged with the age and gender of the patient. Transfer learning is employed by using pre-trained neural network architecture. InceptionV3 architecture is used in the present work, and the difference between the actual and predicted age obtained is 5.921 months.