The RSNA Pediatric Bone Age Machine Learning Challenge

Safwan S. Halabi, Luciano M. Prevedello, Jayashree Kalpathy-Cramer, Artem B. Mamonov, Alexander Bilbily, Mark Cicero, Ian Pan, Lucas Araújo Pereira, Rafael Teixeira Sousa, Nitamar Abdala, Felipe Campos Kitamura, Hans H. Thodberg, Leon Chen, George Shih, Katherine Andriole, Marc D. Kohli, Bradley J. Erickson, Adam E. Flanders

Research output: Contribution to journalArticle

42 Scopus citations

Abstract

Purpose The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show an application of machine learning (ML) and artificial intelligence (AI) in medical imaging, promote collaboration to catalyze AI model creation, and identify innovators in medical imaging. Materials and Methods The goal of this challenge was to solicit individuals and teams to create an algorithm or model using ML techniques that would accurately determine skeletal age in a curated data set of pediatric hand radiographs. The primary evaluation measure was the mean absolute distance (MAD) in months, which was calculated as the mean of the absolute values of the difference between the model estimates and those of the reference standard, bone age. Results A data set consisting of 14 236 hand radiographs (12 611 training set, 1425 validation set, 200 test set) was made available to registered challenge participants. A total of 260 individuals or teams registered on the Challenge website. A total of 105 submissions were uploaded from 48 unique users during the training, validation, and test phases. Almost all methods used deep neural network techniques based on one or more convolutional neural networks (CNNs). The best five results based on MAD were 4.2, 4.4, 4.4, 4.5, and 4.5 months, respectively. Conclusion The RSNA Pediatric Bone Age Machine Learning Challenge showed how a coordinated approach to solving a medical imaging problem can be successfully conducted. Future ML challenges will catalyze collaboration and development of ML tools and methods that can potentially improve diagnostic accuracy and patient care.

Original languageEnglish (US)
Pages (from-to)498-503
Number of pages6
JournalRadiology
Volume290
Issue number2
DOIs
StatePublished - Feb 1 2019
Externally publishedYes

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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    Halabi, S. S., Prevedello, L. M., Kalpathy-Cramer, J., Mamonov, A. B., Bilbily, A., Cicero, M., Pan, I., Pereira, L. A., Sousa, R. T., Abdala, N., Kitamura, F. C., Thodberg, H. H., Chen, L., Shih, G., Andriole, K., Kohli, M. D., Erickson, B. J., & Flanders, A. E. (2019). The RSNA Pediatric Bone Age Machine Learning Challenge. Radiology, 290(2), 498-503. https://doi.org/10.1148/radiol.2018180736