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Predicting Periodontal Treatment Success Using Machine Learning in Periodontitis Patients

NCT07485946 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 86

Last updated 2026-03-25

No results posted yet for this study

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.

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

Sponsors & Collaborators

  • Akdeniz University

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-08-02
Primary Completion
2026-01-31
Completion
2026-01-31

Countries

  • Turkey (Türkiye)

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07485946 on ClinicalTrials.gov