Object Detection for Caries or Pit and Fissure Sealing Requirement in Children's First Permanent Molars

TL;DR

This paper develops an AI-based method using YOLO models to automatically detect caries and sealing requirements in children's molars from smartphone photos, enabling non-experts to identify oral health issues early.

Contribution

It introduces a novel application of YOLO object detection models with tiling strategy for oral health assessment using smartphone images, and deploys it as a mobile app for home use.

Findings

YOLOX with tiling achieves 72.3 mAP.

YOLOv5s6 attains 70.9 mAP with tiling.

The system is deployable on mobile devices as a WeChat applet.

Abstract

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.