Convolutional neural network-based diagnosis of the relationship between mandibular third molar and mandibular nerve canal - ZHANG Jinping, YU Xian, CHEN Yiming, WANG Zehui, TAO Yu, WEI Yi, LI Birong, ZHU Bingzhen, ZHANG Juan (Kouqiang yixue)
Objective To develop an automated system that can accurately determine the relationship between the mandibular third molar and the mandibular nerve canal from panoramic images. Methods A dataset consisting of 600 panoramic images of the oral cavity was selected, and the positions of the mandibular third molar and the mandibular nerve canal were accurately labeled. We compared the research designed TI-YOLOv5 with PANet, Faster R-CNN, Mask R-CNN, ResNeSt-101, and the original YOLOv5 in image segmentation tasks, with evaluation metrics of AP and AP50. Results TI-YOLOv5 achieved AP(average precision) 54.0% and AP50 94.9%, an increase of 4.9 and 6.7 percentage points respectively compared to the original YOLOv5 (AP 49.1%, AP50 88.2%), and surpassed other SOTA methods such as Mask R-CNN (AP 45.1%, AP50 84.2%). Conclusion TI-YOLOv5 is significantly superior to mainstream networks in automatic positioning and relationship classification of mandibular wisdom teeth and neural tubes, with high detection accuracy and discrimination accuracy, and can provide reliable technical support for preoperative risk assessment of mandibular wisdom tooth extraction.