Earticle

초록

영어

Inferior alveolar nerve (IAN) injury is a severe complication associated with mandibular third molar (MM3) extraction. Consequently, the likelihood of IAN injury must be assessed before performing such an extraction. However, existing deep learning methods for classifying the likelihood of IAN injury that rely on mask images often suffer from limited accuracy and lack of interpretability. In this paper, we propose an automated system based on panoramic radiographs, featuring a novel segmentation model SS-TransUnet and classification algorithm CD-IAN injury class. Our objective was to enhance the precision of segmentation of MM3 and the mandibular canal (MC) and classification accuracy of the likelihood of IAN injury, ultimately reducing the occurrence of IAN injuries and providing a certain degree of interpretable foundation for diagnosis. The proposed classification algorithm achieved an accuracy of 0.846, surpassing deep learningbased models by 3.8 %, confirming the effectiveness of our system.

목차

Abstract
1. Introduction
2. Related works
3. Methods
3.1. Dataset
3.2. MM3 detection model
3.3. MM3 detection model
3.4. Likelihood of IAN injury classification model
4. Experiment result
5. Conclusions
Acknowledgement
References

키워드

저자

• Ziyang Gong [ Dept. of Computer Engineering Gachon University ]
• Chang Choi [ Dept. of Computer Engineering Gachon University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004