Improving Automated Pediatric Bone Age Estimation Using Ensembles of Models from the 2017 RSNA Machine Learning Challenge.

Combining less-correlated, high-performing models resulted in better performance than naively combining the top-performing models. Machine learning competitions within radiology should be encouraged to spur development of heterogeneous models whose predictions can be combined to achieve optimal performance.© RSNA, 2019 Supplemental material is available for this article. See also the commentary by Siegel in this issue.