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

Autor: Akal F; Department of Computer Engineering, Hacettepe University, Ankara, Turkey., Batu ED; Department of Pediatrics, Division of Rheumatology, Ankara Training and Research Hospital, University of Health Sciences, Ankara, Turkey. ezgidenizbatu@yahoo.com.; Department of Pediatrics, Division of Rheumatology, Hacettepe University Faculty of Medicine, Ankara, Turkey. ezgidenizbatu@yahoo.com., Sonmez HE; Department of Pediatrics, Division of Rheumatology, Kanuni Sultan Suleyman Training and Research Hospital, University of Health Sciences, Istanbul, Turkey., Karadağ ŞG; Department of Pediatrics, Division of Rheumatology, Kanuni Sultan Suleyman Training and Research Hospital, University of Health Sciences, Istanbul, Turkey., Demir F; Department of Pediatrics, Division of Rheumatology, Umraniye Training and Research Hospital, University of Health Sciences, Istanbul, Turkey., Ayaz NA; Department of Pediatrics, Division of Rheumatology, Kanuni Sultan Suleyman Training and Research Hospital, University of Health Sciences, Istanbul, Turkey., Sözeri B; Department of Pediatrics, Division of Rheumatology, Umraniye Training and Research Hospital, University of Health Sciences, Istanbul, Turkey.
Jazyk: angličtina
Zdroj: Medical & biological engineering & computing [Med Biol Eng Comput] 2022 Dec; Vol. 60 (12), pp. 3601-3614. Date of Electronic Publication: 2022 Oct 20.
DOI: 10.1007/s11517-022-02699-6
Abstrakt: 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.
(© 2022. International Federation for Medical and Biological Engineering.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje