基于卷积神经网络的下颌第三磨牙和下颌神经管关系诊断

doi:10.13591/j.cnki.kqyx.2025.08.006

Abstract

Abstract:

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.

Key words: deep learning, YOLOv5, mandibular third molar, mandibular nerve canal

CLC Number:

R782.1

ZHANG Jinping, YU Xian, CHEN Yiming, WANG Zehui, TAO Yu, WEI Yi, LI Birong, ZHU Bingzhen, ZHANG Juan. Convolutional neural network-based diagnosis of the relationship between mandibular third molar and mandibular nerve canal[J]. Stomatology, 2025, 45(8): 596-602.

Figures/Tables 6

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References 29

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网络结构	AP	AP₅₀
YOLOv5	20.1	88.2
YOLOv5+ Transformer	22.1	92.1
YOLOv5+Transformer+ Fusion	23.4	93.1
YOLOv5+Transformer+Fusion+Involution	25.1	94.1
TI-YOLOv5(上述+CBAM)	29.1	94.9

Convolutional neural network-based diagnosis of the relationship between mandibular third molar and mandibular nerve canal

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Figures/Tables 6

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模型	网络主干	AP	AP₅₀
PANet	ResNet50	40.2	79.8
Faster R-CNN	Res2Net50	38.9	78.1
Mask R-CNN	ResNeXt101	45.1	84.2
ResNeSt-101	ResNeSt-101	45.3	84.5
YOLOv5	CSPDarknet53	49.1	88.2
TI-YOLOV5	CSPDarknet53	54.0	94.9

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