RSNA Pediatric Bone Age Challenge (2017)
As part of its efforts to spur the creation of artificial intelligence (AI) tools for radiology, in 2017 RSNA conducted a challenge to assess bone age from pediatric hand radiographs, a routine task that determines an important developmental indicator.
About the challenge
The challenge used a dataset developed by Stanford University and the University of Colorado, which was annotated by multiple expert observers.
Over 250 participants, comprising radiologists, technology companies, computer scientists, engineers and other medical specialists, competed in the challenge. Participants worked in 37 teams to submit the outcomes of their algorithms. Teams with the most accurate predictions were recognized at RSNA 2017.
- Visit the Pediatric Bone Age Challenge site
- The RSNA Pediatric Bone Age Machine Learning Challenge
Halabi SS, Prevedello LM, Kalpathy-Cramer J et al. Radiology. November 2018; Volume 290, Number 2: 498 - 503
- What Can We Learn from the RSNA Pediatric Bone Age Machine Learning Challenge?
Siegel EL. Radiology. December 2018; Volume 290, Number 2: 504 - 505
- Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs
Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Radiology. November 2017; Volume 287, Number 1: 313-322