Artificial Intelligence Evaluation of Fillings

NCT06022731 | Completed

• 1 other identifier: Retrospective
• Study Type: observational
• Target: 4,323
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this Non-Interventional Clinical Research is to detect the prevalence and distribution of filling and overhanging filling without the need for additional bitewing radiographs using panoramic images, based on a deep CNN (Convolutional Neural Network) architecture trained through supervised learning. In this study, retrospectively obtained radiographs were used in the development of artificial intelligence models for relevant situations. These datasets were obtained from the images of the patients who applied to ESOGU (Eskişehir Osmangazi University) Dentistry Faculty, Dentomaxillofacial Radiology clinic for various dental purposes. Eskisehir Osmangazi University Non-Interventional Clinical Research Ethics Board (decision date and decision number: 04.10.2022/22) approved the study protocol. The principles of the Helsinki Declaration were followed in the study.

Conditions

Dentomaxillofacial Radiology

Keywords

dental filling; dentistry; deep learning,

Outcome Measures

Primary Outcomes (1)

• The success of artificial intelligence models for filling and overhanging filling

  It is obtained by calculating the sensitivity, precision, and F1 scores values for filling and overhanging filling.

  1 year

Study Arms (2)

Filling

Diagnostic Test: Panoramic Radiography

Overhanging Filling

Diagnostic Test: Panoramic Radiography

Interventions

Panoramic Radiography DIAGNOSTIC_TEST

this retrospective study includes analysis of radiographs previously taken from patients for various purposes

Filling; Overhanging Filling

Eligibility Criteria

• Sex: all
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

These datasets were obtained from the images of the patients who applied to ESOGU Dentistry Faculty, Dentomaxillofacial Radiology clinic for various dental purposes.

You may qualify if:

• Images of individuals in the permanent dentition period
• Artifact-free images in the examination region
• Individuals with a history of restorative dental treatment

You may not qualify if:

• Images of individuals in mixed dentition
• Radiographic images obtained by incorrect positioning of the patient or containing artifacts

Sponsors & Collaborators

• Eskisehir Osmangazi University: lead

Study Sites (1)

Eskişehir Osmangazi University

Eskişehir, 26200, Turkey (Türkiye)

Location

MeSH Terms

Interventions

Radiography, Panoramic

Intervention Hierarchy (Ancestors)

Radiography, Dental; Radiography; Diagnostic Imaging; Diagnostic Techniques and Procedures; Diagnosis; Diagnosis, Oral; Dentistry

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associated Professor

Study Record Dates

• First Submitted: August 8, 2023
• First Posted: September 5, 2023
• Study Start: January 1, 2022
• Primary Completion: January 1, 2023
• Study Completion: March 1, 2023
• Last Updated: September 5, 2023

Record last verified: 2023-08

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, CSR

Locations

TU (1)