This paper presents the development of a generative adversarial network (GAN) for the generation of synthetic dental panoramic radiographs. While this is an exploratory study, the ultimate aim is to address the scarcity of data in dental research and education. A deep convolutional GAN (DCGAN) with the Wasserstein loss and a gradient penalty (WGAN-GP) was trained on a dataset of 2322 radiographs of varying quality. The focus of this study was on the dentoalveolar part of the radiographs; other structures were cropped out. Significant data cleaning and preprocessing were conducted to standardize the input formats while maintaining anatomical variability. Four candidate models were identified by varying the critic iterations, number of features and the use of denoising prior to training. To assess the quality of the generated images, a clinical expert evaluated a set of generated synthetic radiographs using a ranking system based on visibility and realism, with scores ranging from 1 (very poor) to 5 (excellent). It was found that most generated radiographs showed moderate depictions of dentoalveolar anatomical structures, although they were considerably impaired by artifacts. The mean evaluation scores showed a trade-off between the model trained on non-denoised data, which showed the highest subjective quality for finer structures, such as the mandibular canal and trabecular bone, and one of the models trained on denoised data, which offered better overall image quality, especially in terms of clarity and sharpness and overall realism. These outcomes serve as a foundation for further research into GAN architectures for dental imaging applications.

The objective of this study was to observe patients’ satisfaction with their in-service direct anterior dental restorations and to compare it with clinical evaluation using FDI (Federation Dental International) criteria. Patients scored their own anterior dental restorations regarding satisfaction (satisfactory /dissatisfactory). If dissatisfaction was mentioned, then, they would be interviewed about the complaint. In the same session, the dental restorations were clinically evaluated by two dentists using FDI criteria (1-5 score) concerning esthetic, functional, and biological domains. Descriptive statistics were used for frequencies of scores attributed by patients and clinicians. In order to compare patients’ to clinicians’ frequencies, the Chi-square test was applied (p ≤ 0.05). A total of 106 restorations were evaluated by patients and clinicians. Patients reported 52.8% of restorations satisfactory and 47.8% dissatisfactory. Overall, clinicians reported the same restorations as 82,3% satisfactory and 17,6% dissatisfactory. Patients’ most frequent complaints referred to color, followed by anatomical form, fracture of material and retention, and approximal anatomical form. Comparing patients’ satisfaction and dissatisfaction rates to clinicians’ evaluation per criteria, there was no difference regarding esthetics. The frequency of dissatisfactory restorations by clinicians was significantly lower when functional and biological properties were compared with patients’ opinions. Direct anterior dental restorations were more frequently reported as satisfactory by patients and clinicians, being the main complaints related to esthetic issues. When clinicians and patients’ evaluations were compared, it was observed that the frequencies of satisfactory restoration by patients and clinicians were similar regarding esthetic properties, and significantly different regarding functional and biological properties.