Enhancing furcation involvement classification on panoramic radiographs with vision transformers.

Journal: BMC oral health
PMID: 39881302

Abstract

BACKGROUND: The severity of furcation involvement (FI) directly affected tooth prognosis and influenced treatment approaches. However, assessing, diagnosing, and treating molars with FI was complicated by anatomical and morphological variations. Cone-beam computed tomography (CBCT) enhanced diagnostic accuracy for detecting FI and measuring furcation defects. Despite its advantages, the high cost and radiation dose associated with CBCT equipment limited its widespread use. The aim of this study was to evaluate the performance of the Vision Transformer (ViT) in comparison with several commonly used traditional deep learning (DL) models for classifying molars with or without FI on panoramic radiographs.

Authors

  • Xuan Zhang
  • Enting Guo
    Division of Computer Science, The University of Aizu, Aizu, Japan.
  • Xu Liu
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore. liuxu16@bjut.edu.cn.
  • Hong Zhao
    Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China.
  • Jie Yang
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
  • Wen Li
  • Wenlei Wu
    Department of Periodontics, Affiliated Hospital of Medical School, Nanjing Stomatological Hospital, Research Institute of Stomatology, Nanjing University, Nanjing, China. wuwenlei6812@163.com.
  • Weibin Sun
    Department of Periodontology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China.

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