End-to-End Bone Age Assessment with Residual Learning

Abstract

Bone age is a reliable metric for determining the level of biological maturity of children and adolescents. Its assessment is a crucial part of the diagnosis of a variety of pediatric syndromes that affect growth, such as endocrine disorders. The most commonly used method for bone age assessment (BAA) is still based on the comparison of the patient's hand and wrist radiograph to a bone age atlas. Such a method, however, takes considerable time, requires an expert rater, and suffers from high inter-rater variability. We present a deep-learning-based approach to estimate bone age from radiographs. It provides a fast, deterministic solution for bone-age assessment. We demonstrate the effectiveness of our method by using it to rate a set of 200 radiographs as part of a contest organized by the Radiological Society of North America. The results of this experiment have shown that our method's performance is similar to the one of a trained physician. Our system is available on-line, providing a free global service for doctors working in remote areas or in institutions with no BAA experts.