1.Convolutional neural network-based diagnosis of the relationship between mandibular third molar and mandibular nerve canal
Jinping ZHANG ; Xian YU ; Yiming CHEN ; Zehui WANG ; Yu TAO ; Yi WEI ; Birong LI ; Bingzhen ZHU ; Juan ZHANG
STOMATOLOGY 2025;45(8):596-602
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 cavi-ty 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 seg-mentation tasks,with evaluation metrics of AP and AP50.Results TI-YOLOv5 achieved AP(average precision)54.0％and AP5094.9％,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 de-tection accuracy and discrimination accuracy,and can provide reliable technical support for preoperative risk assessment of mandibular wisdom tooth extraction.
2.Convolutional neural network-based diagnosis of the relationship between mandibular third molar and mandibular nerve canal
Jinping ZHANG ; Xian YU ; Yiming CHEN ; Zehui WANG ; Yu TAO ; Yi WEI ; Birong LI ; Bingzhen ZHU ; Juan ZHANG
STOMATOLOGY 2025;45(8):596-602
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 cavi-ty 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 seg-mentation tasks,with evaluation metrics of AP and AP50.Results TI-YOLOv5 achieved AP(average precision)54.0％and AP5094.9％,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 de-tection accuracy and discrimination accuracy,and can provide reliable technical support for preoperative risk assessment of mandibular wisdom tooth extraction.
Result Analysis
Print
Save
E-mail