Accuracy of Detection of Dental Caries from Intraoral Images Using Different ArtificiaI Intelligence Models

Cairo University (CU)

Status: Not yet enrolling

Conditions: Intraoral Images; Dental Caries (Diagnosis); Artifical Intelligence

Treatments: Diagnostic Test: FASTER RCNN

Study type: Observational

Funder types: Other

Identifiers

NCT06749743

OP 7-1-1

Details and patient eligibility

About

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?

Enrollment: 398 estimated patients

Sex: All

Ages: 4 to 12 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Child dentition having at least one decayed tooth.

Exclusion criteria

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

Trial design

398 participants in 2 patient groups

training group

Description:

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

Treatment:

Diagnostic Test: FASTER RCNN

test group

Description:

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

Treatment:

Diagnostic Test: FASTER RCNN