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.

Purpose: The retinal blood vessels reflect changes in the brain’s micro-circulation and these changes have been shown to correlate with the incidence of diseases such as stroke, heart disease and Alzheimer’s disease. Studies investigating the retinal vasculature routinely use pupil dilation with tropicamide to optimize image acquisition and quality. The aim of this study was to investigate the effects of tropicamide on retinal vascular parameters using retinal photography. Methods: The study was performed on 41 healthy young subjects of both sexes, using tropicamide to dilate only the right pupil, leaving the left as a control. Results: Pupil dilation with tropicamide resulted in reduced retinal vessel width measures based on standardized approaches, particularly reduced arteriolar caliber (p < .0005). However, closer investigation of the images revealed reduced fundus image magnification in the post-tropicamide images, based on reduced optic nerve head diameter (p < .0005) and longitudinal analysis with image registration and affine transformation (p < .0001). No change in vessel width parameters was observed after adjustment for image magnification. Conclusion: These results suggest that tropicamide does not change the width of the retinal vessels, however width parameters as measured by standard approaches may be reduced due to image magnification changes resulting from cycloplegia. In this study, improved optic nerve head segmentation for image scale conversion removed the magnification error. With this correction, the tropicamide intervention had no effect on vessel width parameters in young healthy people and could be utilized in future without affecting the results of retinal vascular analysis.

Objectives: There is a paucity of research evaluating the use of telehealth applications for preventive oral care, such as remote dental screening and oral health promotion. This study aimed to assess the efficacy of teledentistry in improving the oral health of school-aged children. Methods: In this cluster randomised controlled trial, a total of 175 children aged 4–15 years were enrolled from six schools across Western Australia. The schools were randomly assigned to either the teledentistry or the control group. The intervention consisted of dental screening and oral health promotion. Qualified oral health therapists (OHTs) performed in-person dental examinations on participants in the control group. Different OHTs conducted dental photography-based screenings for participants in the teledentistry group. Both groups received screening reports and educational leaflets. Nine months later, in-person examinations were conducted on all participants in both groups to assess their dental condition and adherence to the dental advice provided at baseline. The primary outcomes included decay experience (dft/DFT index) and the proportion of children converting from a ‘caries-free’ state to a ‘caries-active’ state at the follow-up. Results: A total of 164 children completed the follow-up (mean age, 7.7 ± 2.4 years). At baseline, the prevalence of dental caries in the control and teledentistry groups was 47% and 46%, respectively. The incidence of dental caries in the teledentistry (10%) and control (12%) groups at follow-up was not significantly different (p = 0.7). Conclusion: The findings suggest that teledentistry has comparable efficacy to traditional preventive oral care in maintaining oral health. Teledentistry may offer a viable solution for expanding access to preventive oral care, especially for disadvantaged communities.