Minimum intervention dentistry (MID) is the modern medical approach to the management of caries, utilizing caries risk assessment, and focusing on the early prevention and interception of disease. Moving the focus away from the restoration of teeth allows the dentist to achieve maximum intervention, with minimal invasive treatments. The four core principles of MID can be considered to be: (1) Recognition -early identification and assessment of potential caries risk factors through lifestyle analysis, saliva testing and using plaque diagnostic tests; (2) Reduction -to eliminate or minimize caries risk factors by altering diet and lifestyle habits and increasing the pH of the oral environment; (3) Regeneration -to arrest and reverse incipient lesions, using appropriate topical agents including fluorides and casein phosphopeptides-amorphous calcium phosphates (CPP-ACP); (4) Repair -when cavitation is present and surgical intervention is required, conservative caries removal is carried out to maximize the repair potential of the tooth and retain tooth structure. Bioactive materials are used to restore the tooth and promote internal healing of the dentine. Effective implementation of MID involves integrating each of these four elements into patient assessment and treatment planning. This review paper discusses the key principles of MID as a philosophy of patient care, and the practical objectives which flow into individual patient care.

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.

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After more than 100 years of stability, the landscape of dental practice in Australia has been changed forever by a combination of elements whose effects continue to be felt. Evolving economic cycles, increasing internationalisation, paradigm shifts in work practices and the long-arm of corporatisation have led not only to a number of potential adverse effects but also opportunities to face the challenges of modern dentistry. However, the needs of patients and society in general must not be left behind as the storm passes.

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