The RSNA Pediatric Bone Age Machine Learning Challenge
The RSNA Pediatric Bone Age Machine Learning Challenge
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DOI：
10.1148/radiol.2018180736
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发表时间：
2019-02-01
期刊：
影响因子：
19.7
通讯作者：
Flanders, Adam E.
中科院分区：
文献类型：
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作者：
Halabi, Safwan S.;Prevedello, Luciano M.;Flanders, Adam E.
目的：北美放射学会（RSNA）儿童骨龄机器学习挑战赛旨在展示机器学习（ML）和人工智能（AI）在医学成像中的应用，促进协作以催化AI模型创建，并确定医学成像领域的创新者。材料和方法：本次挑战的目标是邀请个人和团队使用ML技术创建一个算法或模型，以准确地确定儿童手部x光片数据集中的骨骼年龄。主要评价指标为月平均绝对距离（MAD），以模型估计值与参考标准骨龄差绝对值的平均值计算。结果：为注册挑战参与者提供了14 236张手部x线片数据集（12 611张训练集，1425张验证集，200张测试集）。共有260个个人或团队在挑战赛网站上注册。在培训、验证和测试阶段，48个独立用户总共上传了105份提交。几乎所有的方法都使用了基于一个或多个卷积神经网络（cnn）的深度神经网络技术。基于MAD的最佳5个结果分别为4.2个月、4.4个月、4.4个月、4.5个月和4.5个月。结论：RSNA儿童骨龄机器学习挑战赛展示了如何成功地协调解决医学成像问题。未来的ML挑战将促进ML工具和方法的协作和发展，这些工具和方法可能会提高诊断准确性和患者护理。(c) RSNA， 2018年
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. (c) RSNA, 2018