Predicting Periodontal Treatment Success Using Machine Learning in Periodontitis Patients

This retrospective observational study aims to develop treatment-specific machine learning models for predicting tooth-level periodontal treatment outcomes among teeth treated with non-surgical periodontal treatment, conventional flap surgery, or regenerative periodontal surgery. The study uses a multidimensional dataset including baseline clinical periodontal parameters, radiographic findings, documented treatment modalities, and patient-level demographic and clinical characteristics. The analytical unit of the study is the tooth. Only periodontally involved teeth with complete baseline and follow-up clinical records, radiographic assessment, clearly documented treatment modality, and measurable periodontal outcomes are included in the predictive analyses. Full-mouth periodontal information is used for patient-level disease characterization, including periodontal staging and grading according to the 2017 AAP/EFP classification. Because treatment allocation was not randomized, the models are intended to support treatment-specific outcome prediction and clinical interpretability rather than to establish causal superiority between treatment modalities.

NCT Number	NCT07485946
Lead Sponsor	Akdeniz University
Conditions	Periodontitis
Enrollment	126 participants
Start Date	2025-08-02
Primary Completion	2026-01-31 (estimated)
Study Completion	2026-01-31 (estimated)
Updated on ClinicalTrials.gov	2026-07-07