PeriodontitisNCT07485946
Predicting Periodontal Treatment Success Using Machine Learning in Periodontitis Patients
An observational study of Periodontitis, run by Akdeniz University at 1 site in Turkey (Türkiye).
Trial 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. Read more about this study
- Who is it for?
- Ages 16 years and older
- Conditions the study is for
- Periodontitis
- What is being tested?
- Conventional Flap Surgery, Regenerative Flap Surgery and Phase-1 Periodontal Therapy
- Type of treatment
- Pill or oral medicine, Surgery or procedure
- How many people
- 126 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 2 Aug 2025to 31 Jan 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 16 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: The study population consists of patients diagnosed with periodontitis who attended the Department of Periodontology, Faculty of Dentistry, Akdeniz University, between 2021 and 2025. Patients were identified retrospectively from archived clinical and radiographic records. Eligible patients had completed active periodontal therapy and had complete baseline and follow-up records. The analytical unit was the tooth; only periodontally involved teeth with documented treatment modality and measurable treatment outcomes were included in the predictive analyses.
Who may be able to join
- Patients with a confirmed diagnosis of periodontitis according to the 2017 AAP/EFP classification, supported by complete clinical and radiographic records.
- Availability of baseline clinical periodontal examination and radiographic records before periodontal treatment.
- Completion of active periodontal therapy, including non-surgical periodontal treatment and/or surgical periodontal treatment when clinically indicated.
- Availability of at least one post-treatment follow-up visit after completion of active periodontal therapy.
- Presence of at least one periodontally involved tooth meeting tooth-level eligibility criteria.
- Availability of detailed tooth-level documentation, including baseline periodontal measurements, radiographic assessment, documented treatment modality, and corresponding post-treatment outcome records.
- 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.
- 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.
Who cannot join
- Incomplete demographic, clinical, radiographic, treatment, or follow-up records.
- Unclear or undocumented periodontal treatment modality.
- Systemic conditions contraindicating periodontal treatment or substantially affecting periodontal treatment outcomes.
- Pregnancy or breastfeeding at the time of periodontal treatment.
- Ongoing chemotherapy or radiotherapy.
- Current or previous bisphosphonate therapy affecting periodontal or surgical treatment eligibility.
Show all 9Show fewer
- Presence of an immunocompromised condition.
- Acute systemic illness or active infection at the time of periodontal evaluation or treatment.
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Development of a Machine Learning-Assisted Model for Predicting Post-Periodontal Treatment Success and Individual Risk Analysis: A Retrospective Cohort Study
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.
Read the detailed description
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.
- Conditions
- Periodontitis
- Being tested
- Conventional Flap Surgery, Regenerative Flap Surgery, Phase-1 Periodontal Therapy
- Keywords
- periodontitis, machine learning, clinical decision support system
- Medical subject headings
- Periodontitis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Conventional Flap SurgeryProcedure or surgery
Periodontal access flap surgery performed for subgingival debridement and pocket depth reduction in cases unresponsive to Phase-1 therapy.
Regenerative Flap SurgeryProcedure or surgery
Surgical intervention utilizing regenerative materials such as bone grafts or barrier membranes for the treatment of periodontal intrabony defects.
Phase-1 Periodontal TherapyProcedure or surgery
Non-surgical periodontal treatment consisting of scaling and root planing (SRP) under local anesthesia, along with oral hygiene instructions
| Study group | Role | What they receive |
|---|---|---|
| Phase-1 Periodontal Therapy | Patients who received non-surgical periodontal treatment consisting of oral hygiene instructions, scaling, and root planing (SRP). Receives: Procedure: Phase-1 Periodontal Therapy | |
| Conventional Flap Surgery | Patients who underwent traditional periodontal flap surgery (access flap) following unsuccessful non-surgical therapy to reduce pocket depth. Receives: Procedure: Conventional Flap Surgery | |
| Regenerative Flap Surgery | Patients who underwent periodontal surgery involving regenerative materials (bone grafts, membranes, or enamel matrix derivatives) for the treatment of intrabony defects. Receives: Procedure: Regenerative Flap Surgery |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Baseline and final post-treatment follow-up after completion of active periodontal therapy; 12 to 48 months.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Mar 2026
- Study start2 Aug 2025
- Primary completion31 Jan 2026
- Study completion31 Jan 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PeriodontitisStudies of the same condition, open and under way.
- More trials run by Akdeniz UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 126 participants (actual)
Sponsor and investigators
- Lead sponsor
- Akdeniz University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Nezahat Arzu Kayar, Akdeniz University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07485946
- Sponsor's protocol ID
- TBAEK-608
- Other IDs
- TDH-2025-6980
- Status last verified
- Mar 2026
- First submitted
- 17 Mar 2026
- Last updated
- 7 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 7 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of a Machine Learning-Assisted Model for Predicting Post-Periodontal Treatment Success and Individual Risk Analysis: A Retrospective Cohort Study
- ClinicalTrials.gov ID
- NCT07485946
- Acronym / sponsor ID
- TBAEK-608
- Conditions
- Periodontitis
- Keywords
- periodontitis, machine learning, clinical decision support system
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 126 participants (actual)
- Lead sponsor
- Akdeniz UniversityUniversity, hospital or other
- Study start
- 2 Aug 2025 (actual)
- Primary completion
- 31 Jan 2026 (actual)
- Study completion
- 31 Jan 2026 (actual)
- First posted
- 20 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Phase-1 Periodontal Therapy | – | Procedure: Phase-1 Periodontal TherapyPatients who received non-surgical periodontal treatment consisting of oral hygiene instructions, scaling, and root planing (SRP). |
| Conventional Flap Surgery | – | Procedure: Conventional Flap SurgeryPatients who underwent traditional periodontal flap surgery (access flap) following unsuccessful non-surgical therapy to reduce pocket depth. |
| Regenerative Flap Surgery | – | Procedure: Regenerative Flap SurgeryPatients who underwent periodontal surgery involving regenerative materials (bone grafts, membranes, or enamel matrix derivatives) for the treatment of intrabony defects. |
Who may qualify
- Ages
- 16 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study population consists of patients diagnosed with periodontitis who attended the Department of Periodontology, Faculty of Dentistry, Akdeniz University, between 2021 and 2025. Patients were identified retrospectively from archived clinical and radiographic records. Eligible patients had completed active periodontal therapy and had complete baseline and follow-up records. The analytical unit was the tooth; only periodontally involved teeth with documented treatment modality and measurable treatment outcomes were included in the predictive analyses.
Key inclusion criteria (8 listed)
- Patients with a confirmed diagnosis of periodontitis according to the 2017 AAP/EFP classification, supported by complete clinical and radiographic records.
- Availability of baseline clinical periodontal examination and radiographic records before periodontal treatment.
- Completion of active periodontal therapy, including non-surgical periodontal treatment and/or surgical periodontal treatment when clinically indicated.
- Availability of at least one post-treatment follow-up visit after completion of active periodontal therapy.
- Presence of at least one periodontally involved tooth meeting tooth-level eligibility criteria.
Key exclusion criteria (9 listed)
- Incomplete demographic, clinical, radiographic, treatment, or follow-up records.
- Unclear or undocumented periodontal treatment modality.
- Systemic conditions contraindicating periodontal treatment or substantially affecting periodontal treatment outcomes.
- Pregnancy or breastfeeding at the time of periodontal treatment.
- Ongoing chemotherapy or radiotherapy.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Conventional Flap SurgeryPeriodontal access flap surgery performed for subgingival debridement and pocket depth reduction in cases unresponsive to Phase-1 therapy. | Procedure or surgery | Conventional Flap Surgery |
| Regenerative Flap SurgerySurgical intervention utilizing regenerative materials such as bone grafts or barrier membranes for the treatment of periodontal intrabony defects. | Procedure or surgery | Regenerative Flap Surgery |
| Phase-1 Periodontal TherapyNon-surgical periodontal treatment consisting of scaling and root planing (SRP) under local anesthesia, along with oral hygiene instructions | Procedure or surgery | Phase-1 Periodontal Therapy |
Primary outcome
Tooth-level Clinical Success of Periodontal Treatment
Time frame: Baseline and final post-treatment follow-up after completion of active periodontal therapy; 12 to 48 months.
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Jul 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Periodontitis, run by Akdeniz University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 9 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 7 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Tooth-level Clinical Success of Periodontal Treatment (time frame: Baseline and final post-treatment follow-up after completion of active periodontal therapy; 12 to 48 months.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Jul 2026.