Objective: This review is designed to inform future decisions about the benefits of integrating teledentistry into routine health services, by presenting an overview of the evidence for the effectiveness and economic impact of teledentistry. Methods: Two reviewers searched PubMed, EMBASE and CINAHL databases through November 2016 to identify published peer-reviewed studies in English. Teledentistry studies were included if they were; (a) controlled (randomised or non-randomised) assessment studies; and (b) compared outcomes of a teledentistry intervention in terms of clinical or economic evaluation with the outcomes of traditional clinical alternatives. The quality of the studies was evaluated using a quality appraisal tool that considered study performance and design. Results: This review identified 385 publications, of which 217 full-text articles were retrieved for further inspection. Of these, only 11 articles met the inclusion criteria. Nine of the included articles showed some clinical outcomes; the other two were primarily economic analyses. The balance of these studies assesed the efficacy of teledentistry interventions rather than their effectiveness. Four studies (36%) achieved higher quality scores and have greater potential to influence health-care decision-making. To date, the most convincing published evidence regarding the efficacy of teledentistry was provided by studies on paediatric dentistry, orthodontics and oral medicine. The economic analysis referred only to cost-minimisation, suggesting that the use of teleconsultation in dentistry can be cost-saving when compared to a conventional consultation. However, high-quality economic studies on teledentistry are rare. Conclusion: There is emerging evidence supporting the efficacy of teledentistry. However, there is not yet enough conclusive evidence, particularly for its effectiveness, cost-effectiveness and long-term use, to make evidence-based policy decisions on teledentistry.

Objective: This study aimed to evaluate an automated detection system to detect and classify permanent teeth on orthopantomogram (OPG) images using convolutional neural networks (CNNs). Methods: In total, 591 digital OPGs were collected from patients older than 18 years. Three qualified dentists performed individual teeth labelling on images to generate the ground truth annotations. A three-step procedure, relying upon CNNs, was proposed for automated detection and classification of teeth. Firstly, U-Net, a type of CNN, performed preliminary segmentation of tooth regions or detecting regions of interest (ROIs) on panoramic images. Secondly, the Faster R-CNN, an advanced object detection architecture, identified each tooth within the ROI determined by the U-Net. Thirdly, VGG-16 architecture classified each tooth into 32 categories, and a tooth number was assigned. A total of 17,135 teeth cropped from 591 radiographs were used to train and validate the tooth detection and tooth numbering modules. 90% of OPG images were used for training, and the remaining 10% were used for validation. 10-folds cross-validation was performed for measuring the performance. The intersection over union (IoU), F1 score, precision, and recall (i.e. sensitivity) were used as metrics to evaluate the performance of resultant CNNs. Results: The ROI detection module had an IoU of 0.70. The tooth detection module achieved a recall of 0.99 and a precision of 0.99. The tooth numbering module had a recall, precision and F1 score of 0.98. Conclusion: The resultant automated method achieved high performance for automated tooth detection and numbering from OPG images. Deep learning can be helpful in the automatic filing of dental charts in general dentistry and forensic medicine.

Objectives: This study sought to evaluate the efficacy of a mobile teledentistry approach using a smartphone camera for remote screening of dental caries. Materials and Methods: An image acquisition Android App was created to facilitate the acquisition and transmission of dental images to a store-and-forward based telemedicine server. One hundred participants who were attending routine checkups at dental clinics were enrolled in 2014. Following a face-to-face oral screening by a screener (dentist), images of patients' teeth were obtained using a smartphone camera. These images, along with patient information, were then transmitted from the Android App to the server through the Internet for later independent assessment by two charters (off-site dentists). The assessments of these charters were then compared to the benchmark face-to-face caries assessment. Results: Sensitivity values for the photographic method when compared to the benchmark face-to-face caries assessment were moderate, and ranged from 60% to 63%. Weighted kappa (K) as a measure of intragrader agreement for the photographic assessment was estimated as almost perfect (K = 0.84). The intergrader agreement for the photographic method compared to the face-to-face caries assessment ranged from moderate to substantial (K = 0.54–0.66). Conclusions: Despite some limitations, the mobile teledentistry approach has shown the potential to detect occlusal caries from photographs taken by a smartphone camera with an acceptable diagnostic performance compared to traditional face-to-face screening. This study suggests that telemedicine and cellular phone technology can be combined to create an inexpensive and reliable screening tool.