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Predictive modeling for peri-implantitis by using machine learning techniques.

Tomoaki Mameno1, Masahiro Wada2, Kazunori Nozaki3

  • 1Department of Prosthodontics, Gerodontology and Oral Rehabilitation, Osaka University Graduate School of Dentistry, 1-8 Yamadaoka, Suita, Osaka, 565-0871, Japan. mameno@dent.osaka-u.ac.jp.

Scientific Reports
|May 28, 2021
PubMed
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Machine learning models, particularly random forests, can predict peri-implantitis onset with 70% accuracy. Implant function time and oral hygiene are key factors, with complex interactions among risk indicators.

Area of Science:

  • Biomaterials Science
  • Dental Implantology
  • Machine Learning in Healthcare

Background:

  • Peri-implantitis, an inflammatory condition affecting dental implants, poses a significant challenge in long-term implant success.
  • Identifying reliable predictors and understanding risk factor interactions are crucial for preventing peri-implantitis.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting peri-implantitis onset.
  • To identify key risk indicators and analyze their complex interactions in peri-implantitis development.

Main Methods:

  • A retrospective cohort study analyzed 254 implants (127 with and 127 without peri-implantitis) from 1408 implants with ≥4 years in function.
  • Logistic regression, support vector machines, and random forests (RF) were employed to model peri-implantitis prediction.

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  • Demographic data and known risk factors were assessed for their predictive value.

    • Main Results:

    • The random forests (RF) model demonstrated the highest predictive performance (AUC: 0.71, accuracy: 0.70).
    • Implant functional time and oral hygiene emerged as the most influential predictors.
    • Factors like plaque-to-calculus ratio (PCR) >50-60%, smoking >3 cigarettes/day, KMW <2mm, and <2 occlusal supports were associated with increased risk.

    Conclusions:

    • Machine learning, specifically RF, can predict peri-implantitis onset with notable accuracy, accounting for complex, non-linear relationships.
    • Implant functional time and oral hygiene are critical factors, but their interplay with other risk indicators significantly influences peri-implantitis development.