PeriodontitisNCT07113327
Accuracy of Artificial Intelligence-Assisted Staging and Grading for Diagnosis of Periodontitis.
An observational study of Periodontitis, run by Ain Shams University at 1 site in Egypt.
Trial summary
This observational study aims to develop and assess the accuracy, specificity, and sensitivity of a deep learning model for the classification of periodontitis using panoramic radiographs and clinical data inputs. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Periodontitis
- What is being tested?
- Artificial Intelligence-Assisted Staging and Grading for Diagnosis of Periodontitis
- How many people
- 47 participants
- Where
- 1 siteEgypt
- Study dates
- 1 Jul 2024to 1 Jun 2025
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: A total of 47 adult patients diagnosed with periodontitis based on clinical and radiographic criteria were included. All participants were recruited from the outpatient clinics of the Periodontology Department at Ain Shams University. The population consisted of individuals with different stages and grades of periodontitis, providing a diverse testing sample for AI diagnostic validation.
Who may be able to join
- patients with periodontitis causing radiographic bone loss
Who cannot join
- x-ray images with
- Mixed dentition
- Orthodontic brackets
- Images with artifacts and distortion
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Accuracy of Artificial Intelligence-Assisted Staging and Grading for Diagnosis of Periodontitis: A Cross-Sectional Study
This observational study aims to develop and assess the accuracy, specificity, and sensitivity of a deep learning model for the classification of periodontitis using panoramic radiographs and clinical data inputs. A total of 341 panoramic images will be retrospectively collected and labeled by experienced periodontists to train and test the model. The model will be evaluated for its ability to determine the stage and grade of periodontitis based on the 2017 classification guidelines set by the American Academy of Periodontology. The results will be compared to those of clinical experts to validate the AI-assisted diagnostic system. This study is conducted at the Faculty of Dentistry, Ain Shams University, in fulfillment of a Master's degree in Periodontology.
Read the detailed description
a convolutional neural network (CNN)-based deep learning model will be trained using 341 panoramic radiographs and relevant clinical data to classify patients according to stage and grade of periodontitis. Images will be obtained from the Oral and Maxillofacial Radiology Department at Ain Shams University. The inclusion criteria includes radiographs of patients with periodontal bone loss and radiographs of patients with orthodontic brackets, mixed dentition, and artifacts will be excluded. Clinical data, including age, diabetes status, and smoking history, will be incorporated to calculate grading using the bone loss/age ratio of the testing set.
The collected dataset will be divided into 80% for training and 20% for testing. Six anatomical landmarks will be annotated per tooth to calculate the percentage of bone loss mesially and distally, which will be used to determine the stage of disease. Grading will be determined based on percentage bone loss relative to patient age and systemic modifiers. Expert-labeled datasets will serve as a reference standard for evaluating the performance of the AI model.
The primary objective is to evaluate the model's diagnostic accuracy for staging and grading compared to specialist assessments. The secondary objective is to measure the sensitivity and specificity of the model.
- Conditions
- Periodontitis
- Being tested
- Artificial Intelligence-Assisted Staging and Grading for Diagnosis of Periodontitis
- Keywords
- Periodontitis, Artificial Intelligence, Deep Learning
- Medical subject headings
- Periodontitis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence-Assisted Staging and Grading for Diagnosis of PeriodontitisDiagnostic test
A deep learning diagnostic model (using DenseNet and VGG16 architectures) was applied to panoramic radiographs of 47 patients to classify the stage and grade of periodontitis. The model was trained on an external dataset and validated against expert-labeled outcomes. The purpose was to assess the accuracy of AI in replicating clinician-level diagnosis based on the 2017 classification system of periodontitis.
| Study group | Role | What they receive |
|---|---|---|
| Periodontitis Patients: Model's Testing Set | This cohort includes 47 patients diagnosed with different stages and grades of periodontitis. Each participant underwent clinical examination and panoramic radiography. Their images and clinical data were used to validate the performance of a deep learning model developed to classify periodontal staging and grading according to the 2017 classification by the American Academy of Periodontology. No intervention was administered; the study is observational and retrospective in design. Receives: Diagnostic Test: Artificial Intelligence-Assisted Staging and Grading for Diagnosis of Periodontitis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Establishment of an AI model for calculating periodontal bone loss (%) and assigning stage and grade of periodontitis using 2017 classification
Development of a machine learning model using retrospectively collected panoramic radiographs to calculate the percentage of periodontal bone loss (PBL) for each tooth and assign a stage (I-IV) and grade (A-C) of periodontitis according to the 2017 World Workshop classification of Periodontal and Peri-implant Diseases. The outcome will be reported as the accuracy percentage in which the model successfully provides both a PBL calculation and a corresponding stage and grade classification without processing errors.
Measured: 10 months
Secondary outcomes (1)
Diagnostic performance of the AI model compared to specialist diagnosis (accuracy, sensitivity, specificity)
Evaluation of the AI model's diagnostic performance in a testing set of 47 patients whose diagnoses (stage and grade of periodontitis) have been established by two experienced periodontists using combined clinical and radiographic data. The AI model's predictions will be compared with the specialists' consensus diagnosis, and accuracy, sensitivity, and specificity will be calculated as percentages. Results will be aggregated for the overall classification and reported as summary statistics.
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Aug 2025
- Study start1 Jul 2024
- Primary completion30 May 2025
- Study completion1 Jun 2025
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 Ain Shams 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
- Cross-sectional
- Enrolment
- 47 participants (actual)
Sponsor and investigators
- Lead sponsor
- Ain Shams University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Nariman Hesham Hamed Shaker, Ain Shams University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07113327
- Sponsor's protocol ID
- FDASU-REC IM012413
- Status last verified
- Aug 2025
- First submitted
- 1 Aug 2025
- Last updated
- 14 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 14 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Accuracy of Artificial Intelligence-Assisted Staging and Grading for Diagnosis of Periodontitis: A Cross-Sectional Study
- ClinicalTrials.gov ID
- NCT07113327
- Acronym / sponsor ID
- FDASU-REC IM012413
- Conditions
- Periodontitis
- Keywords
- Periodontitis, Artificial Intelligence, Deep Learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 47 participants (actual)
- Lead sponsor
- Ain Shams UniversityUniversity, hospital or other
- Study start
- 1 Jul 2024 (actual)
- Primary completion
- 30 May 2025 (actual)
- Study completion
- 1 Jun 2025 (actual)
- First posted
- 8 Aug 2025 (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 observational study aims to develop and assess the accuracy, specificity, and sensitivity of a deep learning model for the classification of periodontitis using panoramic radiographs and clinical data inputs. A total of 341 panoramic images will be retrospectively collected and labeled by experienced periodontists to train and test the model. The model will be evaluated for its ability to determine the stage and grade of periodontitis based on the 2017 classification guidelines set by the American Academy of Periodontology. The results will be compared to those of clinical experts to validate the AI-assisted diagnostic system. This study is conducted at the Faculty of Dentistry, Ain Shams University, in fulfillment of a Master's degree in Periodontology.
Detailed description
a convolutional neural network (CNN)-based deep learning model will be trained using 341 panoramic radiographs and relevant clinical data to classify patients according to stage and grade of periodontitis. Images will be obtained from the Oral and Maxillofacial Radiology Department at Ain Shams University. The inclusion criteria includes radiographs of patients with periodontal bone loss and radiographs of patients with orthodontic brackets, mixed dentition, and artifacts will be excluded. Clinical data, including age, diabetes status, and smoking history, will be incorporated to calculate grading using the bone loss/age ratio of the testing set.
The collected dataset will be divided into 80% for training and 20% for testing. Six anatomical landmarks will be annotated per tooth to calculate the percentage of bone loss mesially and distally, which will be used to determine the stage of disease. Grading will be determined based on percentage bone loss relative to patient age and systemic modifiers. Expert-labeled datasets will serve as a reference standard for evaluating the performance of the AI model.
The primary objective is to evaluate the model's diagnostic accuracy for staging and grading compared to specialist assessments. The secondary objective is to measure the sensitivity and specificity of the model.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Periodontitis Patients: Model's Testing Set | – | Diagnostic Test: Artificial Intelligence-Assisted Staging and Grading for Diagnosis of PeriodontitisThis cohort includes 47 patients diagnosed with different stages and grades of periodontitis. Each participant underwent clinical examination and panoramic radiography. Their images and clinical data were used to validate the performance of a deep learning model developed to classify periodontal staging and grading according to the 2017 classification by the American Academy of Periodontology. No intervention was administered; the study is observational and retrospective in design. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- A total of 47 adult patients diagnosed with periodontitis based on clinical and radiographic criteria were included. All participants were recruited from the outpatient clinics of the Periodontology Department at Ain Shams University. The population consisted of individuals with different stages and grades of periodontitis, providing a diverse testing sample for AI diagnostic validation.
Key inclusion criteria (1 listed)
- patients with periodontitis causing radiographic bone loss
Key exclusion criteria (4 listed)
- x-ray images with
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 |
|---|---|---|
| Artificial Intelligence-Assisted Staging and Grading for Diagnosis of PeriodontitisA deep learning diagnostic model (using DenseNet and VGG16 architectures) was applied to panoramic radiographs of 47 patients to classify the stage and grade of periodontitis. The model was trained on an external dataset and validated against expert-labeled outcomes. The purpose was to assess the accuracy of AI in replicating clinician-level diagnosis based on the 2017 classification system of periodontitis. | Diagnostic test | Periodontitis Patients: Model's Testing Set |
Primary outcome
Establishment of an AI model for calculating periodontal bone loss (%) and assigning stage and grade of periodontitis using 2017 classification
Time frame: 10 months
Development of a machine learning model using retrospectively collected panoramic radiographs to calculate the percentage of periodontal bone loss (PBL) for each tooth and assign a stage (I-IV) and grade (A-C) of periodontitis according to the 2017 World Workshop classification of Periodontal and Peri-implant Diseases. The outcome will be reported as the accuracy percentage in which the model successfully provides both a PBL calculation and a corresponding stage and grade classification without processing errors.
1 secondary outcome
Diagnostic performance of the AI model compared to specialist diagnosis (accuracy, sensitivity, specificity)
Time frame: 3 months
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 14 Aug 2025.
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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 Ain Shams University at 1 site in Egypt.
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 1 inclusion and 4 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 14 Aug 2025. 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 Egypt. 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?
Establishment of an AI model for calculating periodontal bone loss (%) and assigning stage and grade of periodontitis using 2017 classification (time frame: 10 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 14 Aug 2025.