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

NCT ID: NCT07113327

Last Updated: 2025-08-14

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 47 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-07-01
• Study Completion Date: 2025-06-01

Brief 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.

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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: CROSS_SECTIONAL

Study Groups

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.

Artificial Intelligence-Assisted Staging and Grading for Diagnosis of Periodontitis

Intervention Type: DIAGNOSTIC_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.

Interventions

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• patients with periodontitis causing radiographic bone loss

Exclusion Criteria

• x-ray images with

1. Mixed dentition

2. Orthodontic brackets

3. Images with artifacts and distortion

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Ain Shams University

OTHER

Sponsor Role: lead

Responsible Party

Nariman Hesham Hamed Shaker

Master's Degree Candidate at the Department of Oral Medicine, Periodontology, and Oral Diagnosis

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Ain Shams University

Cairo, , Egypt

Countries

Egypt

Other Identifiers

FDASU-REC IM012413

Identifier Type: -

Identifier Source: org_study_id