Literature DB >> 33569930

Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment.

Byoung Dai Lee1, Mu Sook Lee2.   

Abstract

Bone age assessments are a complicated and lengthy process, which are prone to inter- and intra-observer variabilities. Despite the great demand for fully automated systems, developing an accurate and robust bone age assessment solution has remained challenging. The rapidly evolving deep learning technology has shown promising results in automated bone age assessment. In this review article, we will provide information regarding the history of automated bone age assessments, discuss the current status, and present a literature review, as well as the future directions of artificial intelligence-based bone age assessments.
Copyright © 2021 The Korean Society of Radiology.

Entities:  

Keywords:  Artificial intelligence; Bone age assessment; Convolutional neural network; Deep learning; Left hand and wrist radiographs

Year:  2021        PMID: 33569930     DOI: 10.3348/kjr.2020.0941

Source DB:  PubMed          Journal:  Korean J Radiol        ISSN: 1229-6929            Impact factor:   3.500


  3 in total

1.  Clinical Validation of a Deep Learning-Based Hybrid (Greulich-Pyle and Modified Tanner-Whitehouse) Method for Bone Age Assessment.

Authors:  Kyu-Chong Lee; Kee-Hyoung Lee; Chang Ho Kang; Kyung-Sik Ahn; Lindsey Yoojin Chung; Jae-Joon Lee; Suk Joo Hong; Baek Hyun Kim; Euddeum Shim
Journal:  Korean J Radiol       Date:  2021-10-01       Impact factor: 3.500

2.  Artificial Intelligence-Assisted Bone Age Assessment to Improve the Accuracy and Consistency of Physicians With Different Levels of Experience.

Authors:  Xi Wang; Bo Zhou; Ping Gong; Ting Zhang; Yan Mo; Jie Tang; Xinmiao Shi; Jianhong Wang; Xinyu Yuan; Fengsen Bai; Lei Wang; Qi Xu; Yu Tian; Qing Ha; Chencui Huang; Yizhou Yu; Lin Wang
Journal:  Front Pediatr       Date:  2022-02-24       Impact factor: 3.418

3.  A comparative study of three bone age assessment methods on Chinese preschool-aged children.

Authors:  Chengcheng Gao; Qi Qian; Yangsheng Li; Xiaowei Xing; Xiao He; Min Lin; Zhongxiang Ding
Journal:  Front Pediatr       Date:  2022-08-16       Impact factor: 3.569

  3 in total

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