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Diagnosing growing pains in children by using machine learning: a cross-sectional multicenter study.

Fuat Akal1, Ezgi D Batu2,3, Hafize Emine Sonmez4

  • 1Department of Computer Engineering, Hacettepe University, Ankara, Turkey.

Medical & Biological Engineering & Computing
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PubMed
Summary

Growing pains (GP) are common in children. Machine learning models accurately diagnosed GP by analyzing patient characteristics, potentially aiding clinical diagnosis.

Keywords:
Artificial intelligenceDiagnosisGrowing painsMachine learning

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Area of Science:

  • Pediatric Rheumatology
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Growing pains (GP) represent the most frequent cause of recurrent musculoskeletal pain in children.
  • Currently, there are no established diagnostic criteria for GP, leading to diagnostic challenges.

Purpose of the Study:

  • To analyze characteristics associated with GP.
  • To develop and validate machine learning (ML) models for assisting GP diagnosis.

Main Methods:

  • A cohort study enrolled 398 children with GP and 254 controls with other limb pain conditions.
  • Tenfold cross-validation was employed to train and test ML models for GP classification.
  • The Random Forest algorithm was evaluated for its diagnostic performance.

Main Results:

  • GP commonly presents as bilateral, nocturnal lower extremity pain, often causing nighttime awakenings.
  • Symptoms like arthritis, trauma history, morning stiffness, limping, activity limitation, and school absence were more frequent in controls.
  • The Random Forest ML model achieved high accuracy (0.98), sensitivity (0.99), and specificity (0.97) for diagnosing GP.

Conclusions:

  • This study represents the largest cohort investigating GP and the first to utilize ML for diagnosis.
  • The developed ML model demonstrates significant potential to aid in the clinical diagnosis of growing pains in children.