Data-driven AI platform for dens evaginatus detection on orthodontic intraoral photographs.

Journal: BMC oral health
PMID: 40025464

Abstract

BACKGROUND: The aim of our study was to develop and evaluate a deep learning model (BiStageNet) for automatic detection of dens evaginatus (DE) premolars on orthodontic intraoral photographs. Additionally, based on the training results, we developed a DE detection platform for orthodontic clinical applications.

Authors

  • Ruiyang Ren
    State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China.
  • Jialing Liu
    State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
  • Shihao Li
    Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.
  • Xiaoyue Wu
    State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China.
  • Xingchen Peng
    Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Wen Liao
    School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan 412007, China.
  • Zhihe Zhao
    State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China.

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