A Yolo-V5 Approaches to Evaluation of Filling and Overhanging Filling: An Artificial Intelligence Study
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- Enrollment
- 4,323
- Locations
- 1
- Primary Endpoint
- The success of artificial intelligence models for filling and overhanging filling
Study Overview
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.
Study Design
- Study Type
- Observational
- Observational Model
- Case Only
- Time Perspective
- Retrospective
Eligibility Criteria
- Sex
- All
- Accepts Healthy Volunteers
- No
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
Outcomes
Primary Outcomes
The success of artificial intelligence models for filling and overhanging filling
Time Frame: 1 year
It is obtained by calculating the sensitivity, precision, and F1 scores values for filling and overhanging filling.
Secondary Outcomes
No secondary outcomes reported
Investigators
Elif Bilgir
Associated Professor
Eskisehir Osmangazi University