RSNA Announces Pediatric Bone Age Machine Learning Challenge

The Radiological Society of North America (RSNA) is organizing a challenge intended to show the application of machine learning and artificial intelligence on medical imaging and the ways in which these emerging tools and methodologies may improve diagnostic care. The RSNA Pediatric Bone Age Machine Learning Challenge addresses a familiar image analysis activity for pediatric radiologists: assessment of bone age from hand radiographs of pediatric patients used to evaluate growth and diagnose developmental disorders. The Challenge uses a dataset of hand radiographs provided by a consortium of leading research institutions -- Stanford University, the University of California, Los Angeles and the University of Colorado -- that have associated bone age assessments provided by multiple expert observers. Participants in the challenge will be judged by how well the bone age evaluations produced by their algorithms accord with the expert observers' evaluations. Participants will have the opportunity to directly compare their algorithms in a structured way using this carefully curated dataset.