Development and validation of diagnostic prediction models for central precocious puberty in girls based on machine learning: a multicenter retrospective study - Report - 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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Clinical Report: Machine Learning Models for Diagnosing Central Precocious Puberty

Overview

This study developed machine learning-based diagnostic prediction models for central precocious puberty (CPP) in girls, utilizing data from 2148 patients across four centers in China. The support vector machine (SVM) model showed the best performance with an AUC of 0.850 in internal validation and 0.827 in external validation.

Background

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Data Highlights

ModelAUC (Internal Validation)AUC (External Validation)Accuracy (Internal Validation)Average Accuracy (External Validation)
SVM0.8500.82778.6%72.1%

Key Findings

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Clinical Implications

The SVM model provides a non-invasive tool for diagnosing CPP, potentially streamlining the diagnostic process in clinical settings. By utilizing baseline patient characteristics, healthcare providers may improve diagnostic accuracy and reduce reliance on traditional invasive methods.

Conclusion

The study demonstrates the feasibility of using machine learning models for the diagnosis of CPP, with the SVM model showing promising results in both internal and external validations.

Related Resources & Content

  1. The Journal of Clinical Endocrinology & Metabolism, 2026 -- Central precocious puberty: an Endocrine Society clinical practice guideline
  2. Frontiers in Pediatrics, 2025 -- A serum exosomal four-miRNA signature for the diagnosis of central precocious puberty: a discovery and validation study
  3. BMC Psychiatry, 2026 -- Creation and assessment of a machine learning framework for detecting individuals at elevated risk for psychotic disorders through analysis of medical records
  4. The Journal of Clinical Endocrinology & Metabolism — Association Between Early Onset Puberty and Mental Health Disorders: Findings from a Nationwide Cohort Analysis Utilizing Prospective Registry Data
  5. Frontiers in Medicine — Integrating Machine Learning and Clinicopathological Data to Stratify Survival Risk in Young Women with Localized Breast Cancer
  6. Central precocious puberty: an Endocrine Society clinical practice guideline | The Journal of Clinical Endocrinology & Metabolism | Oxford Academic
  7. Screening for central precocious puberty by single basal Luteinizing Hormone levels | Endocrine | Springer Nature Link

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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