Predictive modeling for step II therapy response in periodontitis - model development and validation.

Journal: NPJ digital medicine
Published Date: Jul 15, 2025

Abstract

Steps I and II periodontal therapy is the first-line treatment for periodontal disease, but has varying success. This study aimed to develop machine learning models to predict changes in periodontal probing depth (PPD) after step II therapy using patient-, tooth-, and site-specific clinical covariates. Models accurately predicted that healthy sites stay healthy, but performed suboptimally for diseased sites. Tuning improved performance, with PPD, tooth-site, and tooth-type identified as key predictors. Pocket closure was predicted with fair accuracy, with baseline PPD as the most relevant covariate. Models predicted improving pockets well but underperformed for non-responding sites, with antibiotic treatment and tooth type being the most influential features. While predictive performance for step II periodontal therapy based on routine clinical data remains limited, models can stratify periodontal sites into meaningful categories and estimate the probability of pocket improvement. They provide a foundation for site-specific outcome prediction and may support patient communication and expectations.

Authors

  • Elias Walter
    Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, Goethestraße 70, 80336 Munich, Germany.
  • Tobias Brock
    Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, GoethestraSSe 70, Munich, Bavaria, Germany.
  • Pierre Lahoud
    OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven and Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven, Kapucijnenvoer 33, 3000, Leuven, Belgium.
  • Nils Werner
    Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, GoethestraSSe 70, Munich, Bavaria, Germany.
  • Felix Czaja
    Department of Statistics, LMU Munich, Munich, Bavaria, Germany.
  • Antonín Tichý
    Institute of Dental Medicine, First Faculty of Medicine of the Charles University and General University Hospital, Prague, Czech Republic.
  • Caspar Bumm
    Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, GoethestraSSe 70, Munich, Bavaria, Germany.
  • Andreas Bender
    Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK ab454@cam.ac.uk.
  • Ana Castro
    Division of Periodontology and Oral Microbiology, Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, Leuven, Belgium.
  • Wim Teughels
    KU Leuven and Dentistry, Department of Oral Health Sciences, Periodontology and Oral Microbiology, Catholic University of Leuven, University Hospitals Leuven, Leuven, Belgium.
  • Falk Schwendicke
    Department of Operative and Preventive Dentistry, Charité - Universitätsmedizin Berlin, Berlin, Germany. falk.schwendicke@charite.de.
  • Matthias Folwaczny
    Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, GoethestraSSe 70, Munich, Bavaria, Germany.

Keywords

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