作者
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
发表日期
2019/2
期刊
Radiology
卷号
290
期号
2
页码范围
498-503
出版商
Radiological Society of North America
简介
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 …
学术搜索中的文章
SS Halabi, LM Prevedello, J Kalpathy-Cramer… - Radiology, 2019