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Determination of growth and developmental stages in hand–wrist radiographs: Can fractal analysis in combination with artificial intelligence be used?

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Full Text / Tam Metin (1.464Mb)

Erişim

info:eu-repo/semantics/closedAccess

Tarih

2024

Yazar

Gonca, Merve
Sert, Mehmet Fatih
Günaçar, Dilara Nil
Köse, Taha Emre
Beşer, Büşra

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Künye

Gonca, M., Sert, M. F., Gunacar, D. N., Kose, T. E., & Beser, B. (2024). Determination of growth and developmental stages in hand-wrist radiographs : Can fractal analysis in combination with artificial intelligence be used?. Ermittlung von Wachstums- und Entwicklungsstadien in Handwurzel-Röntgenaufnahmen : Kann die Fraktalanalyse in Kombination mit künstlicher Intelligenz eingesetzt werden?. Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie, 10.1007/s00056-023-00510-1. Advance online publication. https://doi.org/10.1007/s00056-023-00510-1

Özet

Purpose: The goal of this work was to assess the classification of maturation stage using artificial intelligence (AI) classifiers. Methods: Hand–wrist radiographs (HWRs) from 1067 individuals aged between 7 and 18 years were included. Fifteen regions of interest were selected for fractal dimension (FD) analysis. Five predictive models with different inputs were created (model 1: only FD; model 2: FD and Chapman sesamoid stage; model 3: FD, age, and sex; model 4: FD, Chapman sesamoid stage, age, and sex; model 5: Chapman sesamoid stage, age, and sex). The target diagnoses were accelerating growth velocity, very high growth velocity, and decreasing growth velocity. Four AI algorithms were applied: multilayer perceptron (MLP), support vector machine (SVM), gradient boosting machine (GBM) and C 5.0 decision tree classifier. Results: All AI algorithms except for C 5.0 yielded similar overall predictive accuracies for the five models. In order from lowest to highest, the predictive accuracies of the models were as follows: model 1 < model 3 < model 2 < model 5 < model 4. The highest overall F1 score, which was used instead of accuracy especially for models with unbalanced data, was obtained for models 1, 2, and 3 based on SVM, for model 4 based on MLP, and for model 5 based on C 5.0. Adding Chapman sesamoid stage, chronologic age, and sex as additional inputs to the FD values significantly increased the F1 score. Conclusion: Applying FD analysis to HWRs is not sufficient to predict maturation stage in growing patients but can be considered a growth rate prediction method if combined with the Chapman sesamoid stage, age, and sex.

Kaynak

Journal of Orofacial Orthopedics

Bağlantı

https://doi.org/10.1007/s00056-023-00510-1
https://hdl.handle.net/11436/8754

Koleksiyonlar

  • DŞHF, Klinik Bilimler Bölümü Koleksiyonu [244]
  • PubMed İndeksli Yayınlar Koleksiyonu [2443]
  • Scopus İndeksli Yayınlar Koleksiyonu [5931]
  • WoS İndeksli Yayınlar Koleksiyonu [5260]



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