Journal of Dental Medicine

Background and Aims: Xerostomia is a clinical condition that can affect the quality and quantity of saliva. Saliva is considered an important factor in retention of edentulous patient’s denture wearers. Thus, increasing the prevalence of xerostomia in modern societies is considered a limiting factor in the quality of denture retention. This article reviews the most common techniques of denture manufacturing in edentulous patients with xerostomia and investigates the advantages versus disadvantages of each.
Materials and Methods: In this review, PubMed and google scholar search engines were searched for the following keywords: Flexible Denture, Artificial Saliva Reservoir, Hyposalivation, Hypofunction, and Xerostomia. We evaluated the flexible dentures and dentures with artificial saliva reservoirs in both jaws along with their advantages and disadvantages. 10 articles, specifically discussing complete denture fabrication in patients were selected.
Conclusion: Follow-up results showed that the flexible dentures and split dentures with saliva reservoirs to be effective in improving the quality of life of these patients and therefore can be a considered as a successful treatment option in the prosthetic rehabilitation of these patients.

Background and Aims: Dental caries is one of the most prevalent chronic oral diseases worldwide. Timely and accurate diagnosis of dental caries plays a crucial role in preventing lesion progression and reducing complications. This study aimed to systematically review the studies on dental caries detection using machine learning algorithms applied to periapical radiographs.
Materials and Methods: A comprehensive search was conducted in PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar databases up to the end of 2024. Inclusion criteria comprised studies using machine learning algorithms for detecting dental caries in periapical or intraoral radiographs. The quality of studies was assessed using the QUADAS-2 tool.
Results: From 825 initial articles, 13 studies met the inclusion criteria. All studies used Convolutional Neural Networks (CNNs) with various architectures including ResNet, VGG, Inception, DenseNet, and YOLO. ResNet-based models and their hybrid variants showed the best performance with diagnostic accuracy ranging from 82% to 98%. Comparison with human experts in 6 studies revealed that deep learning algorithms demonstrated similar or superior performance.
Conclusion: From the results, deep learning especially convolutional neural networks, had significant potential for improving dental caries detection in periapical radiographs. However, challenges such as limited high-quality training data and generalizability issues need further investigation.