Open AccessClinical Validation of a Deep Learning-Based Hybrid (Greulich-Pyle and Modified Tanner-Whitehouse) Method for Bone Age Assessment
Author(s) -
Kyu-Chong Lee,
Kee-Hyoung Lee,
Chang Ho Kang,
KyungSik Ahn,
Lindsey Yoojin Chung,
JaeJoon Lee,
Suk Joo Hong,
Baek Hyun Kim,
Euddeum Shim
Publication year - 2021
Publication title -
korean journal of radiology/korean journal of radiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.08
H-Index - 57
eISSN - 2005-8330
pISSN - 1229-6929
DOI - 10.3348/kjr.2020.1468
Subject(s) - bone age , intraclass correlation , standard deviation , medicine , confidence interval , machine learning , artificial intelligence , reproducibility , mathematics , statistics , computer science
To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment.