Artificial Intelligence Evaluation of Fillings

• Sponsor: Eskisehir Osmangazi University (Other)
• Overall Status: Completed
• CT.gov ID: NCT06022731
• Collaborator: (none)
• Enrollment: 4,323
• Location: 1
• Actual Duration (Months): 13.9
• Patients Per Site Per Month: 310.3

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
Dentomaxillofacial Radiology	Diagnostic Test: Panoramic Radiography

Study Design

• Study Type: Observational
• Actual Enrollment: 4323 participants
• Observational Model: Case-Only
• Time Perspective: Retrospective
• Official Title: A Yolo-V5 Approaches to Evaluation of Filling and Overhanging Filling: An Artificial Intelligence Study
• Actual Study Start Date: Jan 1, 2022
• Actual Primary Completion Date: Jan 1, 2023
• Actual Study Completion Date: Mar 1, 2023

Arms and Interventions

Arm	Intervention/Treatment
Filling	Diagnostic Test: Panoramic Radiography; this retrospective study includes analysis of radiographs previously taken from patients for various purposes
Overhanging Filling	Diagnostic Test: Panoramic Radiography; this retrospective study includes analysis of radiographs previously taken from patients for various purposes

Outcome Measures

Primary Outcome Measures

1. The success of artificial intelligence models for filling and overhanging filling [1 year]

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

Eligibility Criteria

Criteria

• Ages Eligible for Study: N/A and Older
• Sexes Eligible for Study: All

Inclusion Criteria:

• Images of individuals in the permanent dentition period

• Artifact-free images in the examination region

• Individuals with a history of restorative dental treatment

Exclusion Criteria:

• Images of individuals in mixed dentition

• Radiographic images obtained by incorrect positioning of the patient or containing artifacts

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Eskişehir Osmangazi University	Eskişehir		Turkey	26200

Sponsors and Collaborators

• Eskisehir Osmangazi University

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: Elif Bilgir, Associated Professor, Eskisehir Osmangazi University
• ClinicalTrials.gov Identifier: NCT06022731
• Other Study ID Numbers: Retrospective
• First Posted: Sep 5, 2023
• Last Update Posted: Sep 5, 2023
• Last Verified: Aug 1, 2023

• Individual Participant Data (IPD) Sharing Statement: Yes
• Plan to Share IPD: Yes
• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No
• Keywords provided by Elif Bilgir, Associated Professor, Eskisehir Osmangazi University: dental filling; dentistry; deep learning,

Study Results

No Results Posted as of Sep 5, 2023