Accuracy Of Detection Of Dental Caries From Intraoral Images Using Different ArtificiaI Intelligence Models

NCT06749743 | Not Yet Recruiting

• 1 other identifier: OP 7-1-1
• Study Type: observational
• Target: 398
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this observational study is to evaluate the diagnostic accuracy of different deep learning models in detecting dental caries from intra oral images taken by a professional intra oral camera in children. The main question it aims to answer is: What is the diagnostic accuracy of different deep learning models in detecting dental caries from intra oral images taken by a professional intra oral camera in children compared to the conventional clinical visual examination?

Conditions

Dental Caries (Diagnosis); Artifical Intelligence; Intraoral Images

Keywords

artificial intelligence; dental caries; diagnosis; intraoral images

Outcome Measures

Primary Outcomes (1)

• Accuracy Of Dental Caries Detection From Intraoral Images Using Different Artificial Intelligence Models Versus Conventional Visual Examination Among A Group Of Children: A Diagnostic Accuracy Study

  Diagnostic accuracy of index tests will be determined, including sensitivity, specificity, overall accuracy, positive and negative predictive values and ROC curve analysis.

  one year

Study Arms (2)

training group

images used to train the AI models on detection of dental caries from intraoral images.

Diagnostic Test: FASTER RCNN

test group

images used to test the accuracy of the AI models in diagnosis of dental caries from intraoral images.

Diagnostic Test: FASTER RCNN

Interventions

FASTER RCNN DIAGNOSTIC_TEST

train artificial intelligence models ( FASTER RCNN, YOLOY ) to detect dental caries , then test their accuracy

Also known as: YOLO

test group; training group

Eligibility Criteria

• Age: 4 Years - 12 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17)
• Sampling Method: Non-Probability Sample

Study Population

Any child with at least one decayed tooth present at the time of recruitment at the Pediatric dental department diagnostic center, Faculty of Dentistry, Cairo University.

You may qualify if:

• Child dentition having at least one decayed tooth.

You may not qualify if:

• Child dentition with developmental enamel defects.
• Children with any systemic medical condition.
• Parent / child refuse to participate in the study.
• Uncooperative child.

Sponsors & Collaborators

• Cairo University: lead

Study Sites (1)

Cairo university

Giza, Giza Governorate, Egypt

Location

MeSH Terms

Conditions

Dental Caries; Disease

Condition Hierarchy (Ancestors)

Tooth Demineralization; Tooth Diseases; Stomatognathic Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: principal investigator

Study Record Dates

• First Submitted: December 19, 2024
• First Posted: December 27, 2024
• Study Start: April 30, 2025
• Primary Completion: December 30, 2025
• Study Completion: December 30, 2025
• Last Updated: March 4, 2025

Record last verified: 2024-12

Locations

EG (1)