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Accuracy of Artificial Intelligence-Assisted Staging and Grading for Diagnosis of Periodontitis.

NCT07113327 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 47

Last updated 2025-08-14

No results posted yet for this study

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. 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.

Conditions

  • Periodontitis

Interventions

DIAGNOSTIC_TEST

Artificial Intelligence-Assisted Staging and Grading for Diagnosis of Periodontitis

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.

Sponsors & Collaborators

  • Ain Shams University

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-07-01
Primary Completion
2025-05-30
Completion
2025-06-01

Countries

  • Egypt

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 NCT07113327 on ClinicalTrials.gov