Details

Autor(en) / Beteiligte

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

Titel

The RSNA Pediatric Bone Age Machine Learning Challenge

Ist Teil von

• Radiology, 2019-02, Vol.290 (2), p.498-503

Ort / Verlag

United States

Erscheinungsjahr

2019

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Siegel in this issue.