Dentomaxillofacial Radiology
Conditions
Keywords
dental filling, dentistry, deep learning,
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.
Interventions
DIAGNOSTIC_TESTPanoramic Radiography
this retrospective study includes analysis of radiographs previously taken from patients for various purposes
Sponsors
Eskisehir Osmangazi University
Study design
Observational model
CASE_ONLY
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
Turkey (Türkiye)
Outcome results
None listed