  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). A consensus dataset of 197
images, annotated jointly by the same six dentist, was used for evaluation. A
five-fold cross validation scheme was used to evaluate the performance of the
AI models.
  Results: he trained models show an increase in average precision and
F1-score, and decrease of false negative rate, with respect to the dental
clinicians. When compared against the dental clinicians, the YOLOv5 model shows
the largest improvement, reporting 0.647 mean average precision, 0.548 mean
F1-score, and 0.149 mean false negative rate. Whereas the best annotators on
each of these metrics reported 0.299, 0.495, and 0.164 respectively.
  Conclusion: Deep-learning models have shown the potential to assist dental
professionals in the diagnosis of caries. Yet, the task remains challenging due
to the artifacts natural to the bitewing images.
