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Rethinking Greulich and Pyle: A Deep Learning Approach to Pediatric Bone Age Assessment Using Pediatric Trauma Hand Radiographs.重新审视格吕利希和派尔方法：一种使用小儿创伤手部X光片进行小儿骨龄评估的深度学习方法。
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Improving Automated Pediatric Bone Age Estimation Using Ensembles of Models from the 2017 RSNA Machine Learning Challenge.利用2017年RSNA机器学习挑战赛的模型集成改进自动儿科骨龄估计
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The RSNA Pediatric Bone Age Machine Learning Challenge.RSNA 儿科骨龄机器学习挑战赛。
Radiology. 2019 Feb;290(2):498-503. doi: 10.1148/radiol.2018180736. Epub 2018 Nov 27.
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Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs.深度学习神经网络模型在评估小儿手部 X 光片骨骼成熟度中的性能。
Radiology. 2018 Apr;287(1):313-322. doi: 10.1148/radiol.2017170236. Epub 2017 Nov 2.
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Fully Automated Deep Learning System for Bone Age Assessment.用于骨龄评估的全自动深度学习系统。
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Validation and reference values of automated bone age determination for four ethnicities.四种族人群的自动骨龄判定的验证和参考值。
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Effect of knowledge of chronologic age on the variability of pediatric bone age determined using the Greulich and Pyle standards.知晓实际年龄对采用格-派标准测定的儿童骨龄变异性的影响。
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Reliability of skeletal age assessments.骨骼年龄评估的可靠性。
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Taking Matters into Your Own Hands.

作者信息

Halabi Safwan S

机构信息

Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, MC 5105, Stanford, CA 94305.

出版信息

Radiol Artif Intell. 2020 Jul 29;2(4):e200150. doi: 10.1148/ryai.2020200150. eCollection 2020 Jul.

DOI:10.1148/ryai.2020200150
PMID:33939791
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8082398/
Abstract
摘要