The uncovered biases and errors in clinical determination of bone age by using deep learning models
The uncovered biases and errors in clinical determination of bone age by using deep learning models
About this item
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Author / Creator
Bai, Mei , Gao, Liangxin , Ji, Min , Ge, Jianbang , Huang, Lingyun , Qiao, HaoChen , Xiao, Jing , Chen, Xiaotian , Yang, Bin , Sun, Yingqi , Zhang, Minjie , Zhang, Wenjie , Luo, Feihong , Yang, Haowei , Mei, Haibing and Qiao, Zhongwei
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Subjects
More information
Scope and Contents
Contents
Objectives
To evaluate AI biases and errors in estimating bone age (BA) by comparing AI and radiologists’ clinical determinations of BA.
Methods
We established three deep learning models from a Chinese private dataset (CHNm), an American public dataset (USAm), and a joint dataset combining the above two datasets (JOIm). The test data CHNt...
Alternative Titles
Full title
The uncovered biases and errors in clinical determination of bone age by using deep learning models
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_miscellaneous_2756123983
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_2756123983
Other Identifiers
ISSN
1432-1084,0938-7994
E-ISSN
1432-1084
DOI
10.1007/s00330-022-09330-0
