Predicting Periodontal Treatment Success Using Machine Learning in Periodontitis Patients

Akdeniz University126 enrolled

Overview

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.

Periodontitis is a chronic, multifactorial inflammatory disease characterized by progressive destruction of the supporting periodontal tissues. Although contemporary periodontal classification systems provide a structured framework for diagnosis, staging, and grading, prediction of treatment response remains challenging because outcomes may vary according to patient-level characteristics, local tooth-level conditions, defect morphology, baseline periodontal status, and treatment modality. Periodontal treatment may include non-surgical periodontal therapy, conventional flap surgery, or regenerative periodontal surgery, depending on clinical indication and local periodontal findings. In routine clinical practice, treatment decisions are individualized and based on clinical examination, radiographic assessment, defect characteristics, and clinician judgment. However, the ability to predict treatment response before or during treatment planning remains limited. This retrospective observational study uses archived clinical and radiographic records to develop treatment-specific machine learning models for predicting periodontal treatment outcomes at the tooth level. The study focuses on periodontally involved teeth with documented treatment modality and measurable follow-up outcomes. Baseline clinical periodontal parameters, radiographic findings, treatment modality, and relevant patient-level characteristics are used to support outcome prediction and model interpretability. The purpose of the study is not to establish causal superiority between treatment modalities, but to evaluate whether machine learning models can provide clinically interpretable, treatment-specific predictions of periodontal treatment response. Explainable artificial intelligence methods are used to identify variables contributing to model predictions and to support future development of personalized periodontal treatment planning.

Interventions

Conventional Flap SurgeryPROCEDURE

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

Regenerative Flap SurgeryPROCEDURE

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

Phase-1 Periodontal TherapyPROCEDURE

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

Eligibility

Sex: ALLMin age: 16 Years

Medical Language ↔ Plain English

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.

Outcomes

Primary Outcomes

Tooth-level Clinical Success of Periodontal Treatment

Binary tooth-level classification of periodontal treatment outcome as clinical success or clinical failure after completion of active periodontal therapy. The outcome was assessed for each eligible periodontally treated tooth by comparing baseline (T0) and final follow-up (T1) clinical records. Tooth-level clinical treatment success was defined as the simultaneous presence of residual probing pocket depth (PPD) ≤4 mm and absence of bleeding on probing (BOP) at the T1 (final follow-up) examination. As a secondary, machine-learning-oriented outcome, treatment response was categorized using a ≥50% relative reduction in PPD between baseline (T0) and final follow-up (T1), with cases meeting this threshold labeled 'high responders. This primary clinical outcome was analyzed separately from the machine-learning classification outcome based on the ≥50% probing pocket depth reduction threshold.

Time frame: Baseline and final post-treatment follow-up after completion of active periodontal therapy; 12 to 48 months.