Journal of Periodontal and Implant Science

Korean Academy of Periodontology (대한치주과학회)
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Determination of the stage and grade of periodontitis according to the current classification of periodontal and peri-implant diseases and conditions (2018) using machine learning algorithms

  • Kubra Ertas (Department of Periodontology, Faculty of Dentistry, Suleyman Demirel University) ;
  • Ihsan Pence (Department of Software Engineering, Bucak Faculty of Technology, Burdur Mehmet Akif Ersoy University) ;
  • Melike Siseci Cesmeli (Department of Software Engineering, Bucak Faculty of Technology, Burdur Mehmet Akif Ersoy University) ;
  • Zuhal Yetkin Ay (Department of Periodontology, Faculty of Dentistry, Suleyman Demirel University)
  • Received : 2022.02.18
  • Accepted : 2022.07.26
  • Published : 2023.02.28
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Abstract

Purpose: The current Classification of Periodontal and Peri-Implant Diseases and Conditions, published and disseminated in 2018, involves some difficulties and causes diagnostic conflicts due to its criteria, especially for inexperienced clinicians. The aim of this study was to design a decision system based on machine learning algorithms by using clinical measurements and radiographic images in order to determine and facilitate the staging and grading of periodontitis. Methods: In the first part of this study, machine learning models were created using the Python programming language based on clinical data from 144 individuals who presented to the Department of Periodontology, Faculty of Dentistry, Süleyman Demirel University. In the second part, panoramic radiographic images were processed and classification was carried out with deep learning algorithms. Results: Using clinical data, the accuracy of staging with the tree algorithm reached 97.2%, while the random forest and k-nearest neighbor algorithms reached 98.6% accuracy. The best staging accuracy for processing panoramic radiographic images was provided by a hybrid network model algorithm combining the proposed ResNet50 architecture and the support vector machine algorithm. For this, the images were preprocessed, and high success was obtained, with a classification accuracy of 88.2% for staging. However, in general, it was observed that the radiographic images provided a low level of success, in terms of accuracy, for modeling the grading of periodontitis. Conclusions: The machine learning-based decision system presented herein can facilitate periodontal diagnoses despite its current limitations. Further studies are planned to optimize the algorithm and improve the results.

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References

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