Email Alert RSS
Volume 12 Issue 3
May  2021
Turn off MathJax
Article Contents
Abstract
References
Related Articles
CIDAN Wangjiu, LABA Dunzhu, WANG Fengdan, GU Xiao, CHEN Shi, LIU Yongliang, SHI Lei, PAN Hui, YIN Wu, JIN Zhengyu. Comparison of Three Methods of Assessing the Bone Age in Tibetan Children and the Features of Their Skeletal Maturity[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(3): 411-416. DOI: 10.12290/xhyxzz.20200259
Citation: CIDAN Wangjiu, LABA Dunzhu, WANG Fengdan, GU Xiao, CHEN Shi, LIU Yongliang, SHI Lei, PAN Hui, YIN Wu, JIN Zhengyu. Comparison of Three Methods of Assessing the Bone Age in Tibetan Children and the Features of Their Skeletal Maturity[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(3): 411-416. DOI: 10.12290/xhyxzz.20200259
shu

Comparison of Three Methods of Assessing the Bone Age in Tibetan Children and the Features of Their Skeletal Maturity

  • 1.

    Department of Radiology, Tibet Autonomous Region People's Hospital, Lhasa 850000, China

  • 2.

    Department of Radiology, Peking Union Medical College Hospital, Chinese Academy ofMedical Sciences & Peking Union Medical College, Beijing 100730, China

  • 3.

    Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy ofMedical Sciences & Peking Union Medical College, Beijing 100730, China

  • 4.

    Hangzhou YITU Healthcare Technology, Hangzhou 310000, China

Funds: 

the Youth Fund of National Natural Science Foundation of China 82001900

the Specialized Supporting Fund of Beijing for Young Excellent Talents 

the Group Aiding Medical Program of Natural Science Foundation of Tibet XZ2020ZR-ZY01(Z)

More Information
  • Corresponding author:

    WANG Fengdan Tel: 86-10-69159608, E-mail: wangfengdan@pumch.cn

  • Received Date: September 28, 2020
  • Accepted Date: November 23, 2020
  • Issue Publish Date: May 29, 2021
    Graphical Abstract
    • Abstract
  •   Objective  The aim of this study is to evaluate which of the three methods of assessing the bone age (BA), Greulich-Pyle (GP) atlas, Tanner-Whitehouse3 (TW3) and Chinese Hand Wrist Standard TW-China05, is most appropriate for Tibetan children, and to further investigate the BA characteristics of modern Tibetan children.
      Methods  Radiographs of the left hand of Tibetan children aged 4 to 18 years who presented with trauma to Tibet Autonomous Region People's Hospital between September 2013 and November 2019 were retrospectively collected. BAs of these radiographs were analyzed by two experienced reviewers based on the GP atlas who came from Peking Union Medical College Hospital. A previously reported artificial-intelligence (AI) BA system was used for the TW3(including TW3-RUS and TW3-Carpal) and TW-China05 method. The Pearson correlation method was used to analyze the correlation between calendar age and BA determined by GP atlas, TW3 and TW-China05 methods.
      Results  There were 305 Tibetan children (209 boys and 96 girls) with a mean calendar age of 11.22±4.81 years included in this study. Pearson correlation analysis showed that the BAs measured by the GP atlas, TW3-RUS, TW3-Carpal and TW-China05 methods are highly correlated with the calendar ages of Tibetan children, and the GP atlas has the strongest correlation (r=0.961), followed by TW3-RUS method (r=0.941), TW-China05 method (r=0.937), and TW3-Carpal method(r=0.895). From 4- to 10-year-old, the BAs of all Tibetan boys and girls were smaller than their calendar age with a difference degrees; subsequently, BAs showed a tendency of catch-up during puberty, but still lagging behind calendar ages from 16- to 18-years old.
      Conclusions  Compared with the TW3 and TW-China05 methods, GP atlas may be the most accurate method of BA assessment for Tibetan children. BAs of modern Tibetan children shows catch-up trend during adolescence, but still lag behind calendar ages by the age of 18.
    • FullText(HTML)
    • References (25)
  • [1]
    Martin DD, Wit JM, Hochberg Z, et al. The use of bone age in clinical practice-part 1[J]. Horm Res Paediatr, 2011, 76: 1-9. http://content.karger.com/ProdukteDB/produkte.asp?Aktion=ShowPDF&ArtikelNr=329372&Ausgabe=255394&ProduktNr=224036&filename=329372.pdf
    [2]
    Creo AL, Schwenk WF. Bone age: A handy tool for pediatric providers[J]. Pediatrics, 2017, 140: e20171486. DOI: 10.1542/peds.2017-1486
    [3]
    Greulich WW, Pyle SI. Radiographic atlas of skeletal development of hand wrist[M]. Stanford: Stanford Universtiy Press, 1971.
    [4]
    Tanner JM, Healy MJR, Goldstein H, et al. Assessment of skeletal maturity and prediction of adult height (TW3 method)[M]. 3rd ed. London: WB Saunders, 2001.
    [5]
    张绍岩, 马振国, 沈勋章, 等. 中国人手腕骨发育标准——中华05. IV. 中国儿童手腕骨发育特征[J]. 中国运动医学杂志, 2007, 26: 452-455. DOI: 10.3969/j.issn.1000-6710.2007.04.014

    Zhang SY, Ma ZG, Shen XZ, et al. The skeletal development standards of hand and wrist for Chinese Children—China 05. IV.Features of hand and wrist for Chinese children[J]. Zhongguo Yun Dong Yi Xue Za Zhi, 2007, 26: 452-455. DOI: 10.3969/j.issn.1000-6710.2007.04.014
    [6]
    Gertych A, Zhang A, Sayre J, et al. Bone age assessment of children using a digital hand atlas[J]. Comput Med Imag Grap, 2007, 31: 322-331. DOI: 10.1016/j.compmedimag.2007.02.012
    [7]
    Bianba B, Yangzong Y, Gonggalanzi G, et al. Anthropometric measures of 9- to 10-year-old native Tibetan children living at 3700 and 4300 m above sea level and Han Chinese living at 3700 m[J]. Medicine (Baltimore), 2015, 94: e1516. DOI: 10.1097/MD.0000000000001516
    [8]
    Wang F, Gu X, Chen S, et al. Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development[J]. Peer J, 2020, 8: e8854. DOI: 10.7717/peerj.8854
    [9]
    Van RR, Lequin MH, Robben SG, et al. Is the Greulich and Pyle atlas still valid for Dutch Caucasian children today?[J]. Pediatr Radiol, 2001, 31: 748-752. DOI: 10.1007/s002470100531
    [10]
    Bken B, Safak AA, Yazici B, et al. Is the assessment of bone age by the Greulich-Pyle method reliable at forensic age estimation for Turkish children?[J]. Forensic Sci Int, 2007, 173: 146-153. DOI: 10.1016/j.forsciint.2007.02.023
    [11]
    Zhang A, Sayre JW, Vachon L, et al. Racial differences in growth patterns of children assessed on the basis of bone age[J]. Radiology, 2009, 250: 228-235. DOI: 10.1148/radiol.2493080468
    [12]
    Mirza WA, Memon M, Mahmood SM, et al. Bone age practices in infants and older children among practicing radiologists in Pakistan: developing world perspective[J]. Cureus, 2019, 11: e3936. http://www.researchgate.net/publication/330550531_Bone_Age_Practices_in_Infants_and_Older_Children_among_Practicing_Radiologists_in_Pakistan_Developing_World_Perspective
    [13]
    Govender D, Goodier RM. Bone of contention: the applicability of the Greulich-Pyle method for skeletal age assessment in South Africa[J]. SA J Radiol, 2018, 22: 1348-1354. http://www.ncbi.nlm.nih.gov/pubmed/31754503
    [14]
    Zafar AM, Nadeem N, Husen Y, et al. An appraisal of Greulich-Pyle Atlas for skeletal age assessment in Pakistan[J]. J PakMed Assoc, 2010, 60: 552-555.
    [15]
    Proos LA, Nnerholm LT, Jonsson B, et al. Can the TW3 bone age determination method provide additional criteria for growth hormone treatment in adopted girls with early puberty? A comparison of the Tanner-Whitehouse 3 method with the Greulich-Pyle and the Tanner-Whitehouse 2 methods[J]. Horm Res Paediatr, 2010, 73: 35-40. DOI: 10.1159/000271914
    [16]
    Lynnerup N, Belard E, Buch-Olsen K, et al. Intra- and interobserver error of the Greulich-Pyle method as used on a Danish forensic sample[J]. Forensic Sci Int, 2008, 179: 242. e1-6. http://europepmc.org/abstract/MED/18602233
    [17]
    李春山, 李长勇, 席焕久, 等. 拉萨藏族青少年手腕部骨龄发育评价[J]. 中国临床康复, 2005, 9: 36-38. https://www.cnki.com.cn/Article/CJFDTOTAL-XDKF200523021.htm

    Li CS, Li CY, Xi HJ, et al. Assessment of development of wrist skeletal age of Tibet adolescent in Lhasa[J]. Zhongguo Lin Chuang Kang Fu, 2005, 9: 36-38. https://www.cnki.com.cn/Article/CJFDTOTAL-XDKF200523021.htm
    [18]
    李春山, 李长勇, 任甫, 等. 那曲地区藏族青少年腕部骨龄评价[J]. 解剖学杂志, 2006, 29: 414-416. https://www.cnki.com.cn/Article/CJFDTOTAL-JPXZ200604010.htm

    Li CS, Li CY, Ren F, et al. Assessment of Tibetan adolescents skeletal age of wrist in Naqu district[J]. Jie Pou Xue Za Zhi, 2006, 29: 414-416. https://www.cnki.com.cn/Article/CJFDTOTAL-JPXZ200604010.htm
    [19]
    Dang S, Yan H, Wang D. Implication of World Health Organization growth standards on estimation of malnutrition in young Chinese children: Two examples from rural western China and the Tibet region[J]. J Child Health Care, 2014, 18: 358-368. DOI: 10.1177/1367493513496669
    [20]
    Argnani L, Cogo A, Gualdi-Russso E. Growth and nutri-tional status of Tibetan children at high altitude[J]. Coll Antropol, 2008, 32: 807-812. http://www.ncbi.nlm.nih.gov/pubmed/18982755
    [21]
    Tajmir SH, Lee H, Shailam R, et al. Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability[J]. Skeletal Radiol, 2019, 48: 275-283. DOI: 10.1007/s00256-018-3033-2
    [22]
    Spampinato C, Palazzo S, Giordano D, et al. Deep learning for automated skeletal bone age assessment in X-ray images[J]. Med Image Anal, 2017, 36: 41-51. DOI: 10.1016/j.media.2016.10.010
    [23]
    Lee H, Tajmir S, Lee J, et al. Fully automated deep learning system for bone age assessment[J]. J Digit Imaging, 2017, 30: 427-441. DOI: 10.1007/s10278-017-9955-8
    [24]
    Larson DB, Chen MC, Lungren MP, et al. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs[J]. Radiology, 2018, 287: 313-322. DOI: 10.1148/radiol.2017170236
    [25]
    Zhou XL, Wang EG, Lin Q, et al. Diagnostic performance of convolutional neural network-based Tanner-Whitehouse 3 bone age assessment system[J]. Quant Imaging Med Surg, 2020, 10: 657-667. DOI: 10.21037/qims.2020.02.20
    • Related Articles

  • Related Articles

    [1]LIU Qixing, WANG Huogen, CIDAN Wangjiu, TUDAN Awang, YANG Meijie, PUQIONG Qiongda, YANG Xiao, PAN Hui, WANG Fengdan. Construction and Validation of A Deep Learning-based Bone Age Prediction Model for Children Living in Both Plain and Highland Regions[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(6): 1439-1446. DOI: 10.12290/xhyxzz.2023-0651
    [2]LUO Xufei, LYU Han, SONG Zaiwei, LIU Hui, WANG Zhixiang, LI Haodong, WANG Ye, ZHU Di, ZHANG Lu, CHEN Yaolong. The Impact of Generative Artificial Intelligence on the Development, Evaluation, and Application of Clinical Practice Guidelines[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(5): 1173-1181. DOI: 10.12290/xhyxzz.2024-0602
    [3]FU Yifan, WENG Guihu, CAO Zhe, ZHANG Taiping. Application of Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cancer[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(4): 747-750. DOI: 10.12290/xhyxzz.2024-0256
    [4]LU Yao, LIU Jianing, WANG Mian, HUANG Jiajie, HAN Baojin, SUN Mingyao, CHENG Qianji, NING Jinling, GE Long. Artificial Intelligence in Shared Decision Making[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(3): 661-667. DOI: 10.12290/xhyxzz.2023-0209
    [5]LIU Shuai, ZHU Huadong. Application of Artificial Intelligence in Cardiopulmonary Resuscitation[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(3): 453-458. DOI: 10.12290/xhyxzz.2022-0711
    [6]LI Yang, DU Leilei, XU Fei, LI Yixuan, QIAO En. Big Data and Artificial Intelligence in Medicine[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(1): 184-189. DOI: 10.12290/xhyxzz.2022-0182
    [7]WU Jun, FEI Sijia, SHEN Bo, ZHANG Hanwen, HUANG Jianfeng, PAN Qi, ZHAO Jianchun, DING Dayong. Artificial Intelligence Analysis of Nerve Fibers Based on Corneal Confocal Microscopy[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 736-741. DOI: 10.12290/xhyxzz.2021-0510
    [8]WANG Yao, SUN Hongli, ZHAO Ying, ZHANG Li, ZHU Renyuan, DOU Hongtao, YUAN Ying, LIU Yali, LIU Wenjing, XU Yingchun. Accuracy Assessment of the Morphological Analysis System with Automated Microscopy and Artificial Intelligence for Gram-stained Vaginal Discharge Smears[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(4): 503-509. DOI: 10.12290/xhyxzz.2021-0412
    [9]Rui-feng LIU, Yu XIA, Yu-xin JIANG. Application of Artificial Intelligence in Ultrasound Medicine[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(5): 453-457. DOI: 10.3969/j.issn.1674-9081.2018.05.015
    [10]Zheng-yu JIN. Prospects and Challenges:when Medical Imaging Meets Artificial Intelligence[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(1): 2-4. DOI: 10.3969/j.issn.1674-9081.2018.01.001

  • Cited by

    Periodical cited type(5)

    1. 次旦旺久，土旦阿旺，杨美杰，普琼穷达，王凤丹，潘慧，金征宇. 海拔高度对儿童及青少年骨龄发育的影响. 基础医学与临床. 2023(04): 636-640 .
    2. 拉巴顿珠，次旦旺久，边巴次仁，王凤丹，陈适，李正，吴红，次旦旺姆，潘慧，银武，金征宇. 人工智能骨龄系统提高西藏放射科医师骨龄判读的准确性. 医学影像学杂志. 2023(11): 2061-2065 .
    3. 邓成清，孙安林，唐宇轩，张萍，唐爱群. 人工智能软件在儿童发育相关疾病骨龄评估中的临床应用. 中华养生保健. 2023(22): 54-57 .
    4. 彭丽珍，陈俊杰，顾林，廖敏. 中华05法和G-P图谱法在儿童青少年骨龄评估中的效果对比研究. 影像研究与医学应用. 2022(20): 86-88 .
    5. 许可，宁刚. 基于人工智能的骨龄辅助评价系统对四川地区完全性生长激素缺乏症患儿骨龄研究. 中华妇幼临床医学杂志(电子版). 2022(04): 449-459 .

    Other cited types(0)

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (815) PDF downloads (30) Cited by(5)
    Related
    Links: National Health Commission of the People's Republic of China Chinese Academy of Medical Sciences(CAMS) AMLC Check CNKI Database CNKI Term Online Chinese General Practice
    Address：Room 302,Third Floor,Building 7, No. 1 Shuaifuyuan, Wangfujing, Dongcheng District, Beijing 100730, China
    Tel：010-69154261/4262
    E - mail：medj@pumch.cn mjpumch@126.com
    Copyright of website：Editorial Office of Medical Journal of Peking Union Medical College Hospital

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return
    x Close Forever Close