NCT07485946

Predicting Periodontal Treatment Success Using Machine Learning in Periodontitis Patients

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 86 (actual)
• Sponsor: Akdeniz University · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

Summary

The aim of this study is to develop a clinical decision-support model capable of predicting the optimal periodontal treatment option at the individual patient level by utilizing a multidimensional dataset composed of clinical periodontal parameters, radiographic findings, implemented treatment modalities, and demographic characteristics. In this context, the study seeks to strengthen personalized treatment planning by identifying the most effective therapeutic approach for individuals presenting for periodontal care.

Detailed description

Periodontitis is a highly prevalent, complex, and multifactorial chronic inflammatory condition affecting the gingiva, periodontal ligament, cementum, and alveolar bone, in which a microbially driven, host-mediated immune-inflammatory response ultimately results in periodontal attachment loss and alveolar bone resorption. The diagnosis of periodontitis relies on a thorough clinical and radiographic assessment of the periodontal tissues. The characterization of the disease commonly includes the number and proportion of teeth presenting probing pocket depths exceeding specific thresholds (most frequently \>4 mm with bleeding on probing and ≥6 mm), the number of teeth lost due to periodontitis, the number of teeth exhibiting intrabony defects, and the number of teeth with furcation involvement, all of which serve as clinically meaningful indicators.The classification update released in 2017 transformed periodontal diagnostics by adopting a stage-and-grade system, which allows clinicians to evaluate disease severity, anticipated progression, and the likelihood of future relapse with greater precision. Although these criteria effectively identify established disease, they primarily reflect historical tissue destruction and provide limited insight into current disease activity or future progression. Consequently, there is growing interest in more sensitive, specific, and non-invasive diagnostic approaches that can improve early detection and prognostic accuracy. However, despite this structured approach, clinicians still face difficulties because periodontitis develops through a highly variable interplay of host immune function, microbial imbalance, genetic factors, and lifestyle or environmental influences. Periodontal treatment is generally divided into non-surgical and surgical approaches. Non-surgical therapy (Phase I treatment) primarily includes supragingival and subgingival debridement procedures, focusing on the removal of dental calculus and the smoothing of root surfaces. In some cases, however, due to disease progression, anatomical complexities, or patient-specific host factors, surgical intervention (Phase II treatment) may become necessary. Surgical treatment options include flap surgery, resective procedures, and regenerative techniques. Although clinical parameters such as probing pocket depth, bleeding on probing, and clinical attachment level can guide the decision to transition from Phase I to Phase II therapy, this decision is often individualized and based on the clinician's expertise and patient-specific considerations. Artificial intelligence represents a field within computer science dedicated to creating systems that can perform tasks typically requiring human cognitive abilities-often more rapidly and with greater precision. Within this field, machine learning (ML) involves developing statistical algorithms that can analyze and categorize data or images, as well as predict risks and outcomes using a variety of computational techniques.Artificial intelligence (AI) applications in periodontology are extensive and primarily focused on enhancing disease classification, diagnosis, and treatment planning. In treatment planning, AI facilitates the segmentation of periodontal structures, allowing clinicians to visualize and simulate surgical outcomes in a virtual environment. The integration of an AI-based model may maximize the likelihood of achieving successful periodontal outcomes and guide periodontists in selecting the most appropriate treatment plan. In light of this potential, the aim of the present study is to develop a decision-support model capable of predicting the optimal periodontal treatment option at the individual patient level by using advanced machine learning algorithms on a multidimensional dataset comprising clinical periodontal parameters, radiographic data, applied treatment modalities, and demographic information. By doing so, the study seeks to support personalized treatment planning by identifying the most effective therapeutic approach for individuals undergoing periodontal therapy.

Conditions

• Periodontitis

Interventions

Type	Name	Description
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

Timeline

• Start date: 2025-08-02
• Primary completion: 2026-01-31
• Completion: 2026-01-31
• First posted: 2026-03-20
• Last updated: 2026-03-25

Locations

1 site across 1 country: Turkey (Türkiye)