A novel deep learning-based model for automated tooth detection and numbering in mixed and permanent dentition in occlusal photographs.

Journal: BMC oral health
PMID: 40158107

Abstract

BACKGROUND: While artificial intelligence-driven approaches have shown great promise in dental diagnosis and treatment planning, most research focuses on dental radiographs. Only three studies have explored automated tooth numbering in oral photographs, all focusing on permanent dentition. Our study aimed to introduce an automated system for detection and numbering of teeth across mixed and permanent dentitions in occlusal photographs.

Authors

  • Zahra Ghorbani
    Department of Community Oral Health, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Seyed Sepehr Mirebeigi-Jamasbi
    Research Committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshju Blvd., Velenjak St., Chamran Highway, Tehran, 1983963113, Iran. Sepehr78mb@gmail.com.
  • Mohammad Hassannia Dargah
    Research Committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshju Blvd., Velenjak St., Chamran Highway, Tehran, 1983963113, Iran. co.mhdsoft@gmail.com.
  • Mohammad Nahvi
    Research Committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshju Blvd., Velenjak St., Chamran Highway, Tehran, 1983963113, Iran.
  • Sara Alsadat Hosseinikhah Manshadi
    Research Committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshju Blvd., Velenjak St., Chamran Highway, Tehran, 1983963113, Iran.
  • Zeinab Akbarzadeh Fathabadi
    Research Committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshju Blvd., Velenjak St., Chamran Highway, Tehran, 1983963113, Iran.

Keywords

External Resources

Popular Topics
Recent Journals