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
Citation: 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

Construction and Validation of A Deep Learning-based Bone Age Prediction Model for Children Living in Both Plain and Highland Regions

Funds: 

Youth Fund of National Natural Science Foundation of China 82001900

National High Level Hospital Clinical Research Funding 2022-PUMCH-A-003

CAMS Innovation Fund for Medical Sciences 2021-I2M-1-051

More Information
  • Corresponding author:

    WANG Fengdan, E-mail: wangfengdan@pumch.cn

  • Received Date: December 25, 2023
  • Accepted Date: May 26, 2024
  • Available Online: October 11, 2024
  • Publish Date: October 11, 2024
  • Issue Publish Date: November 29, 2024
  • Objective 

    To construct and validate a deep learning-based bone age prediction model for children living in both plain and highland regions.

    Methods 

    A model named "ethnicity vision gender-bone age net (EVG-BANet)" was trained using three datasets, including the Radiology Society of North America (RSNA) dataset [training set(n=12 611), validation set (n=1425), test set (n=200)], the Radiological Hand Pose Estimation (RHPE) dataset[training set (n=5491), validation set (n=713), test set (n=79)], and a self-established dataset[training set (n=825), test set (n=351)], and it was validated using an external test set. Self-established dataset retrospectively recruited 1176 left-hand DR images of children from Peking Union Medical College Hospital (n=745, all were Han) and Tibet Autonomous Region People's Hospital (n=431, 114 were Han, 317 were Tibetan). External test set included images from People's Hospital of Nagqu (n=256, all were Tibetan). Mean absolute difference (MAD) and accuracy within 1 year were used as indicators.

    Results 

    EVG-BANet exhibited MAD of 0.34 and 0.52 years in RSNA and RHPE test sets, respectively. In the self-established test set, the model achieved MAD of 0.47 years (95% CI: 0.43-0.50) with accuracy within 1 year of 97.72% (95% CI: 95.56-99.01%). For the external test set, MAD was 0.53 years(95% CI: 0.48-0.58), with accuracy within 1 year of 89.45% (95% CI: 85.03-92.93).

    Conclusion 

    EVG-BANet demonstrated high accuracy in bone age prediction, and therefore can be applied in children living in both plain and highland.

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