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

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Abstract

Growing pains (GP) are the most common cause of recurrent musculoskeletal pain in children. There are no diagnostic criteria for GP. We aimed at analyzing GP-related characteristics and assisting GP diagnosis by using machine learning (ML). Children with GP and diseased controls were enrolled between February and August 2019. ML models were developed by using tenfold cross-validation to classify GP patients. A total of 398 patients with GP (F/M:1.3; median age 102 months) and 254 patients with other diseases causing limb pain were enrolled. The pain was bilateral (86.2%), localized in the lower extremities (89.7%), nocturnal (74%), and led to awakening at night (60.8%) in most GP patients. History of arthritis, trauma, morning stiffness, limping, limitation of activities, and school abstinence were more prevalent among controls than in GP patients (p = 0.016 for trauma; p < 0.001 for others). The experiments with different ML models revealed that the Random Forest algorithm had the best performance with 0.98 accuracy, 0.99 sensitivity, and 0.97 specificity for GP diagnosis. This is the largest cohort study of children with GP and the first study that attempts to diagnose GP by using ML techniques. Our ML model may be used to facilitate diagnosing GP.

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Data Availability

Python source codes and the pre-trained model are available at GitHub (https://github.com/fuatakal/GrowingPain). The entire dataset used for this study may be available from the corresponding author upon reasonable request.

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Author information

Author notes

  1. Ezgi D. Batu

    Present address: Department of Pediatrics, Division of Rheumatology, Hacettepe University Faculty of Medicine, Ankara, Turkey

  2. Fuat Akal and Ezgi D. Batu contributed equally to this work.

Authors and Affiliations

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

    Fuat Akal

  2. Department of Pediatrics, Division of Rheumatology, Ankara Training and Research Hospital, University of Health Sciences, Ankara, Turkey

    Ezgi D. Batu

  3. Department of Pediatrics, Division of Rheumatology, Kanuni Sultan Suleyman Training and Research Hospital, University of Health Sciences, Istanbul, Turkey

    Hafize Emine Sonmez, Şerife G. Karadağ & Nuray Aktay Ayaz

  4. Department of Pediatrics, Division of Rheumatology, Umraniye Training and Research Hospital, University of Health Sciences, Istanbul, Turkey

    Ferhat Demir & Betül Sözeri

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  1. Fuat Akal
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Contributions

Dr. Akal conceptualized and designed the study, drafted the initial manuscript, and reviewed and revised the manuscript. Drs. Sonmez, Karadag, Demir, Aktay Ayaz, and Sözeri designed the data collection instruments, collected data, carried out the initial analyses, and reviewed and revised the manuscript. Dr. Batu conceptualized and designed the study, coordinated and supervised data collection, and critically reviewed the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Corresponding author

Correspondence to Ezgi D. Batu.

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Consent to participate and for publication

Informed consent was obtained from all children and their parents to participate in the study and for publication.

Previous presentations

This study was presented as an electronic poster at the 26th European Paediatric Rheumatology e-Congress of the Paediatric Rheumatology European Association (PReS), held online on 23–25 September 2020.

Conflict of interest

The authors declare no competing interests.

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Akal, F., Batu, E.D., Sonmez, H.E. et al. Diagnosing growing pains in children by using machine learning: a cross-sectional multicenter study. Med Biol Eng Comput 60, 3601–3614 (2022). https://doi.org/10.1007/s11517-022-02699-6

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  • DOI: https://doi.org/10.1007/s11517-022-02699-6

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