AI-Dentify: deep learning for proximal caries detection on bitewing x-ray - HUNT4 Oral Health Study.

Background: Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the use of artificial intelligence, and in particular deep-learning, has the potential to aid in the diagnosis by providing a quick and informative analysis of the bitewing images.

Methods: A dataset of 13,887 bitewings from the HUNT4 Oral Health Study were annotated individually by six different experts, and used to train three different object detection deep-learning architectures: RetinaNet (ResNet50), YOLOv5 (M size), and EfficientDet (D0 and D1 sizes).

The validity of self-reported number of teeth and edentulousness among Norwegian older adults, the HUNT Study.

Background: Number of teeth is an established indicator of oral health and is commonly self-reported in epidemiological studies due to the costly and labor-intensive nature of clinical examinations. Although previous studies have found self-reported number of teeth to be a reasonably accurate measure, its accuracy among older adults ≥ 70 years is less explored. The aim of this study was to assess the validity of self-reported number of teeth and edentulousness in older adults and to investigate factors that may affect the accuracy of self-reports.