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.

翻译：背景：龋齿诊断需要人工检查患者的诊断性咬翼片，随后对识别出的疑似病变牙位进行视觉检查和探诊。然而，人工智能（尤其是深度学习）的应用有望通过快速提供咬翼片图像的分析信息来辅助诊断。方法：来自HUNT4口腔健康研究的13,887张咬翼片数据集由六位不同专家分别标注，并用于训练三种不同的目标检测深度学习架构：RetinaNet（ResNet50）、YOLOv5（M尺寸）和EfficientDet（D0和D1尺寸）。另由同六位牙医联合标注的197张图像形成共识数据集用于评估，采用五折交叉验证方案评估AI模型性能。结果：与牙科临床医生相比，训练后的模型在平均精确率和F1分数上均有提升，假阴性率降低。在与牙科临床医生的对比中，YOLOv5模型改进最为显著，平均精确率均值为0.647，平均F1分数均值为0.548，平均假阴性率均值为0.149。而对应指标表现最佳的标注者分别为0.299、0.495和0.164。结论：深度学习模型已展现出辅助牙科专业人员诊断龋齿的潜力。然而，由于咬翼片图像固有的伪影，该任务仍具有挑战性。