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.

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

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

The investigators plan to publish the findings obtained as a result of the study in internationally journals and share this information within the publication.