Dental caries is one of the most common oral diseases that, if left untreated, can lead to a variety of oral problems. It mainly occurs inside the pits and fissures on the occlusal/buccal/palatal surfaces of molars and children are a high-risk group for pit and fissure caries in permanent molars. Pit and fissure sealing is one of the most effective methods that is widely used in prevention of pit and fissure caries. However, current detection of pits and fissures or caries depends primarily on the experienced dentists, which ordinary parents do not have, and children may miss the remedial treatment without timely detection. To address this issue, we present a method to autodetect caries and pit and fissure sealing requirements using oral photos taken by smartphones. We use the YOLOv5 and YOLOX models and adopt a tiling strategy to reduce information loss during image pre-processing. The best result for YOLOXs model with tiling strategy is 72.3 mAP.5, while the best result without tiling strategy is 71.2. YOLOv5s6 model with/without tiling attains 70.9/67.9 mAP.5, respectively. We deploy the pre-trained network to mobile devices as a WeChat applet, allowing in-home detection by parents or children guardian.

翻译：龋病是最常见的口腔疾病之一，若未及时治疗可能引发多种口腔问题。龋坏主要发生于磨牙咬合面/颊面/腭面的窝沟深处，而儿童是恒磨牙窝沟龋的高风险群体。窝沟封闭是预防窝沟龋最广泛使用且有效的措施之一。然而，当前窝沟与龋损的检测主要依赖经验丰富的牙科医生，普通家长不具备此项专业能力，若未能及时发现，儿童可能错失最佳治疗时机。针对这一问题，我们提出一种利用智能手机拍摄的口腔照片自动检测龋病及窝沟封闭需求的方法。研究采用YOLOv5与YOLOX模型，并引入分块策略以减少图像预处理过程中的信息损失。采用分块策略的YOLOXs模型最优结果达72.3 mAP.5，未使用分块策略时为71.2 mAP.5；YOLOv5s6模型在采用/未采用分块策略时分别获得70.9/67.9 mAP.5。我们将预训练网络部署至移动设备作为微信小程序，实现家长或监护人的居家检测。