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NCT07485946

Predicting Periodontal Treatment Success Using Machine Learning in Periodontitis Patients

Sponsor: Akdeniz University

View on ClinicalTrials.gov

Summary

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.

Official title: Development of a Machine Learning-Assisted Model for Predicting Post-Periodontal Treatment Success and Individual Risk Analysis: A Retrospective Cohort Study

Key Details

Gender

All

Age Range

16 Years - Any

Study Type

OBSERVATIONAL

Enrollment

126

Start Date

2025-08-02

Completion Date

2026-01-31

Last Updated

2026-07-07

Healthy Volunteers

Conditions

Periodontitis

Interventions

PROCEDURE

Conventional Flap Surgery

Periodontal access flap surgery performed for subgingival debridement and pocket depth reduction in cases unresponsive to Phase-1 therapy.

PROCEDURE

Regenerative Flap Surgery

Surgical intervention utilizing regenerative materials such as bone grafts or barrier membranes for the treatment of periodontal intrabony defects.

PROCEDURE

Phase-1 Periodontal Therapy

Non-surgical periodontal treatment consisting of scaling and root planing (SRP) under local anesthesia, along with oral hygiene instructions

Inclusion Criteria: 1. Patients with a confirmed diagnosis of periodontitis according to the 2017 AAP/EFP classification, supported by complete clinical and radiographic records. 2. Availability of baseline clinical periodontal examination and radiographic records before periodontal treatment. 3. Completion of active periodontal therapy, including non-surgical periodontal treatment and/or surgical periodontal treatment when clinically indicated. 4. Availability of at least one post-treatment follow-up visit after completion of active periodontal therapy. 5. Presence of at least one periodontally involved tooth meeting tooth-level eligibility criteria. 6. Availability of detailed tooth-level documentation, including baseline periodontal measurements, radiographic assessment, documented treatment modality, and corresponding post-treatment outcome records. 7. Teeth were eligible for tooth-level analysis if they received one of the predefined periodontal treatment modalities: non-surgical periodontal treatment, conventional flap surgery, or regenerative periodontal surgery. 8. Patients with a previous history of cancer were eligible if chemotherapy or radiotherapy had been completed and medical clearance for periodontal treatment had been obtained. Exclusion Criteria: 1. Incomplete demographic, clinical, radiographic, treatment, or follow-up records. 2. Unclear or undocumented periodontal treatment modality. 3. Systemic conditions contraindicating periodontal treatment or substantially affecting periodontal treatment outcomes. 4. Pregnancy or breastfeeding at the time of periodontal treatment. 5. Ongoing chemotherapy or radiotherapy. 6. Current or previous bisphosphonate therapy affecting periodontal or surgical treatment eligibility. 7. Presence of an immunocompromised condition. 8. Acute systemic illness or active infection at the time of periodontal evaluation or treatment. 9. Teeth with missing baseline or follow-up periodontal measurements, missing radiographic assessment, unclear treatment allocation, or insufficient documentation for outcome assessment were excluded from the tooth-level analysis.

Locations (1)

Akdeniz University

Antalya, konyaaltı, Turkey (Türkiye)

Clinical Research Directory

Predicting Periodontal Treatment Success Using Machine Learning in Periodontitis Patients

Summary

Key Details

Conditions

Interventions

Eligibility Criteria

Locations (1)