Development and validation of diagnostic prediction models for central precocious puberty in girls based on machine learning: a multicenter retrospective study - Summary - MDSpire

Development and validation of diagnostic prediction models for central precocious puberty in girls based on machine learning: a multicenter retrospective study

  • By

  • Wenyong Wu

  • Zhe Su

  • Haiyan Wei

  • Yanhong Li

  • Benlong Zhu

  • Xin Yuan

  • Daibin Lei

  • Yi Wei

  • Xian Wu

  • Hanghan Ou

  • Xinyu Chen

  • Ziling Zhu

  • Ruimin Chen

  • June 15, 2026

  • 0 min

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Objective:

To develop novel diagnostic prediction models for central precocious puberty (CPP) diagnosis in girls based on machine learning.

Approach:
    Key Findings:
    • Eight independent predictors identified: chronological age, disease course, height SDS, basal luteinizing hormone, bone age - chronological age, height SDS for bone age, uterine volume, and larger ovarian volume.
    • The SVM model showed optimal performance with AUC values of 0.850 in internal validation and 0.827 in external validation.
    • Accuracy rates were 78.6% in internal validation and 72.1% average in external validations.
    Interpretation:

    Limitations:
    • The study is retrospective and may be subject to selection bias.
    • External validation was limited to three test groups from specific centers.
    Conclusion:

    The SVM prediction model demonstrated performance across multiple validation stages.

Original Source(s)

Development and validation of diagnostic prediction models for central precocious puberty in girls based on machine learning: a multicenter retrospective study

  • by Wenyong Wu, Zhe Su, Haiyan Wei, Yanhong Li, Benlong Zhu, Xin Yuan, Daibin Lei, Yi Wei, Xian Wu, Hanghan Ou, Xinyu Chen, Ziling Zhu, Ruimin Chen

  • June 15, 2026

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