Bone Age Assessment for Lower Age Groups Using Triplet Network in Small Dataset of Hand X-Rays

TLDR: In this article, a bone-age assessment model using triplet loss for children in 0-3 years of age is proposed, which achieves an AUC of 0.92 for binary and 0.82 for multi-class classification with visible separation in embedding clusters.

Abstract: Skeletal Bone age assessment is a routine clinical procedure carried out by paediatricians and endocrinologists for investigating a variety of endocrinological, metabolic, genetic and growth disorders in children. Skeletal maturity advances with change in structure and size of the skeletal bones with respect to age. This is commonly done by radiological investigation of the left hand due to its non dominant use. Dissent in the skeletal age and bone age values indicates abnormality. In this study, a bone-age assessment model using triplet loss for children in 0–3 years of age is proposed. Furthermore, this is the first automated bone age assessment study on lower age groups with comparable results, using one tenth of the training data samples as opposed to conventional deep neural networks. We have used small number of radiographs per class from Digital Hand Atlas Database System (DHA), a publicly available comprehensive x-ray dataset. Model trained achieves an AUC of 0.92 for binary and 0.82 for multi-class classification with visible separation in embedding clusters; thereby resulting in correct predictions on test data set.