The present study aims to validate the diagnostic performance and evaluate the reliability of an artificial intelligence system based on the convolutional neural network method for the morphological classification of sella turcica in CBCT (cone-beam computed tomography) images. In this retrospective study, sella segmentation and classification models (CranioCatch, Eskisehir, Türkiye) were applied to sagittal slices of CBCT images, using PyTorch supported by U-Net and TensorFlow 1, and we implemented the GoogleNet Inception V3 algorithm. The AI models achieved successful results for sella turcica segmentation of CBCT images based on the deep learning models. The sensitivity, precision, and F-measure values were 1.0, 1.0, and 1.0, respectively, for segmentation of sella turcica in sagittal slices of CBCT images. The sensitivity, precision, accuracy, and F1-score were 1.0, 0.95, 0.98, and 0.84, respectively, for sella-turcica-flattened classification; 0.95, 0.83, 0.92, and 0.88, respectively, for sella-turcica-oval classification; 0.75, 0.94, 0.90, and 0.83, respectively, for sella-turcica-round classification. It is predicted that detecting anatomical landmarks with orthodontic importance, such as the sella point, with artificial intelligence algorithms will save time for orthodontists and facilitate diagnosis.

Abdelkarim AZ, et al. Imaging Sci Dent. 2019 Mar;49(1):71-77. https://doi.org/10.5624/isd.2019.49.1.71

This comprehensive review assessed the integration of robotics and artificial intelligence (AI) in dentistry, offering a transparent overview of developments across dental fields. Covering articles in prosthodontics, orthodontics, implantology, surgery, and radiology, the review included 39 articles on robotics and 16 on AI. Screening adhered to PRISMA guidelines, with searches conducted on Medline, Google Scholar, and IEEE. Incorporating the search strategy, the review used keywords related to dentistry, robotics, and AI. For robotics, 296 articles were screened, resulting in 39 qualifying for qualitative synthesis. A separate AI search on PubMed identified 142 studies within the last decade, with 16 studies selected for a detailed full-text analysis, offering a consolidated overview of the current state-of-the-art knowledge in the AI domain. Geographic distribution highlighted East Asia as a major research contributor. The findings indicate an increasing trend in dentistry robotics since 2000 and, particularly since 2016, in AI dentistry. The majority of the literature fell under the category of basic research. The technology readiness level did not cross “three” (proof of concept) in 41% of all articles. Therefore, the overall literature quality remains low, particularly regarding clinical validation.

Background: Down syndrome (DS) is a genetic condition characterized by an extra copy of chromosome 21, resulting in various physical and cognitive features. This study aimed to comprehensively analyze the dental and craniofacial morphology of individuals with DS using Cone Beam Computed Tomography (CBCT). Methods: Six individuals with DS, comprising five males and one female aged 17 to 35 years, underwent CBCT scanning. Radiographic assessments included dentition, occlusion, paranasal sinuses, airway, skull bones, and suture calcification. Linear and angular cephalometric measurements were performed, and airway analysis was conducted using Dolphin 3D imaging software v.11. Results: The study revealed prognathic maxilla in five patients, prognathic mandible in four, and bimaxillary protrusion in two. Dental findings included microdontia, enamel hypoplasia, and congenitally missing teeth, with maxillary and mandibular third molars most commonly absent. Sinus abnormalities, delayed suture closure, and cervical spine anomalies were also observed. Conclusion: These findings contribute to a deeper understanding of DS-related craniofacial characteristics and emphasize the importance of considering these morphometric features in clinical management strategies for individuals with DS. This study’s limited sample size underscores the significance of radiographic assessment in planning interventions such as cosmetic reconstructions, prosthetic rehabilitation, or orthodontic treatment for individuals with DS.

Periodontitis is a severe infection that damages the bone surrounding teeth, making severity assessment challenging for dentists. Utilizing artificial intelligence (AI) techniques enhances diagnostic accuracy in radiographic analysis, enabling a more efficient and precise diagnosis. The aim of this research is to develop an AI-based diagnostic system comprising two deep learning stages, designed to efficiently detect and classify periodontitis from panoramic radiographs. In the proposed two-stage system, a binary classifier is first utilized to determine the presence of periodontal bone loss, which is then localized and classified by the second AI model. Based on our extensive research, we concluded that convolutional neural networks (CNNs) are the most effective type of neural network for addressing our problem. To ensure the accuracy and reliability of results, we developed two robust CNN models, YOLOv8 and MobileNet-v2, that have shown significant performance in similar applications. The primary model, MobileNet-v2, was utilized to detect periodontitis in panoramic radiographs, while the secondary model, YOLOv8, was developed to localize the affected regions and classify the severity level of the periodontitis. A custom dataset was created to comprise 817 panoramic images. Our developed YOLOv8 model achieved a testing precision of 0.74 and a recall of 0.7, while the MobileNet-v2 model achieved a testing accuracy of 0.88 and a recall of 1.0. These results were further validated by an oral radiology specialist to ensure their accuracy and reliability. Our proposed system is considered to be efficient, cost-effective, and easy to use compared to existing systems in the literature and similar software services available in the market for enhancing diagnosis of periodontitis in clinical practice.

Fibrous dysplasia is a rare bone disorder characterized by the replacement of normal bone by abnormal fibrous tissue. Here we present a 16-year-old female with a fibrous dysplasia in the maxilla and obliteration of the sinus in the same side. Cone beam computed tomography scan revealed a mixed radiopacity that extended from the alveolar crest of the right posterior teeth to the right orbital floor in the superior-inferior direction. The radiopaque areas had homogenous ground glass appearance. There is a loss of bone trabeculation, thinning of the cortical boundaries but still intact, and a loss of the lamina dura around the right posterior permanent teeth. The radiographical features of the lesion were indicative of fibrous dysplasia in the maxilla.