Literature DB >> 24179234

Bone age assessment: automated techniques coming of age?

R R van Rijn1, H H Thodberg.   

Abstract

Bone age determination from hand radiographs is one of the oldest radiographic procedures. The first atlas was published by Poland in 1898, and to date the Greulich Pyle atlas, although it dates from 1959, is still the most commonly used method. Bone age rating is time-consuming, suffers from an unsatisfactorily high rater variability, and therefore already 25 years ago it was proposed to replace the manual rating by an automated, computerized method, a field nowadays referred to as computer-aided diagnosis (CAD). The pursuit of this goal reached a first stage of accomplishment in 1992-1996 with the presentation of several systems. However, they had limited clinical value, and efforts in CAD research were increasingly focused on lesion detection for cancer screening. It was only in 2008 that a fully-automated bone age method was presented, which appears to be clinically acceptable. In this paper we consider the requirements that should be met by an automated bone age method and review the state of the art. Integration in PACS and saving time are important factors for radiologists. But it is the validation of the methods which poses the greatest challenge, because there is no gold standard for bone age rating, and the direct comparison to manual rating is therefore not sufficient for demonstrating that manual rating can be replaced by automated rating. One needs additional studies assessing the precision of a method and its accuracy when used for adult height prediction, which serves as an objective.

Entities:  

Keywords:  Age determination by skeleton; automated pattern recognition; computer-assisted diagnosis

Mesh:

Year:  2013        PMID: 24179234     DOI: 10.1258/ar.2012.120443

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  15 in total

1.  Forensic use of the Greulich and Pyle atlas: prediction intervals and relevance.

Authors:  K Chaumoitre; B Saliba-Serre; P Adalian; M Signoli; G Leonetti; M Panuel
Journal:  Eur Radiol       Date:  2016-06-29       Impact factor: 5.315

2.  Forensic age estimation for pelvic X-ray images using deep learning.

Authors:  Yuan Li; Zhizhong Huang; Xiaoai Dong; Weibo Liang; Hui Xue; Lin Zhang; Yi Zhang; Zhenhua Deng
Journal:  Eur Radiol       Date:  2018-11-06       Impact factor: 5.315

3.  Deep Learning Lends a Hand to Pediatric Radiology.

Authors:  Ronald M Summers
Journal:  Radiology       Date:  2018-04       Impact factor: 11.105

4.  Assessment of rapidly advancing bone age during puberty on elbow radiographs using a deep neural network model.

Authors:  Kyung-Sik Ahn; Byeonguk Bae; Woo Young Jang; Jin Hyuck Lee; Saelin Oh; Baek Hyun Kim; Si Wook Lee; Hae Woon Jung; Jae Won Lee; Jinkyeong Sung; Kyu-Hwan Jung; Chang Ho Kang; Soon Hyuck Lee
Journal:  Eur Radiol       Date:  2021-06-11       Impact factor: 5.315

Review 5.  A Critical Appraisal of the Effect of Gonadotropin-Releasing Hormon Analog Treatment on Adult Height of Girls with Central Precocious Puberty.

Authors:  Abdullah Bereket
Journal:  J Clin Res Pediatr Endocrinol       Date:  2017-12-27

6.  Diagnostic performance of convolutional neural network-based Tanner-Whitehouse 3 bone age assessment system.

Authors:  Xue-Lian Zhou; Er-Gang Wang; Qiang Lin; Guan-Ping Dong; Wei Wu; Ke Huang; Can Lai; Gang Yu; Hai-Chun Zhou; Xiao-Hui Ma; Xuan Jia; Lei Shi; Yong-Sheng Zheng; Lan-Xuan Liu; Da Ha; Hao Ni; Jun Yang; Jun-Fen Fu
Journal:  Quant Imaging Med Surg       Date:  2020-03

7.  Performance of an artificial intelligence system for bone age assessment in Tibet.

Authors:  Fengdan Wang; Wangjiu Cidan; Xiao Gu; Shi Chen; Wu Yin; Yongliang Liu; Lei Shi; Hui Pan; Zhengyu Jin
Journal:  Br J Radiol       Date:  2021-02-09       Impact factor: 3.039

8.  Maturation Disparity between Hand-Wrist Bones in a Chinese Sample of Normal Children: An Analysis Based on Automatic BoneXpert and Manual Greulich and Pyle Atlas Assessment.

Authors:  Ji Zhang; Fangqin Lin; Xiaoyi Ding
Journal:  Korean J Radiol       Date:  2016-04-14       Impact factor: 3.500

9.  Reducing acquisition time for MRI-based forensic age estimation.

Authors:  Bernhard Neumayer; Matthias Schloegl; Christian Payer; Thomas Widek; Sebastian Tschauner; Thomas Ehammer; Rudolf Stollberger; Martin Urschler
Journal:  Sci Rep       Date:  2018-02-01       Impact factor: 4.379

10.  A simple method for bone age assessment: the capitohamate planimetry.

Authors:  Jung-Ah Choi; Young Chul Kim; Seon Jeong Min; Eun Kyung Khil
Journal:  Eur Radiol       Date:  2018-01-30       Impact factor: 5.315

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