Deep Learning Architecture to Infer Kennedy Classification of Partially Edentulous Arches Using Object Detection Techniques and Piecewise Annotations.

Journal: International dental journal
PMID: 39645471

Abstract

OBJECTIVES: Dental health is integral to overall well-being, with early detection of issues critical for prevention. This research work focuses on utilizing artificial intelligence and deep learning-based object detection techniques for automated detection of common dental issues in orthopantomography x-ray images, including broken roots, periodontally compromised teeth, and the Kennedy classification of partially edentulous arches.

Authors

  • Zohaib Khurshid
    Lecturer, Prosthodontics, Department of Prosthodontics and Dental Implantology, King Faisal University, Al-Ahsa, Saudi Arabia; and Professor, Center of Excellence for Regenerative Dentistry, Department of Anatomy, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand.
  • Maria Waqas
    Department of Computer and Information Systems Engineering, NED University of Engineering and Technology, Karachi, Pakistan. Electronic address: mariaw@cloud.neduet.edu.pk.
  • Shehzad Hasan
    Department of Computer and Information Systems Engineering, NED University of Engineering and Technology, Karachi, Pakistan. Electronic address: shasan@cloud.neduet.edu.pk.
  • Shakeel Kazmi
    Department of Oral Biology, College of Dentistry, Shaheed Zulfiqar Ali Bhutto Medical University Islamabad, Pakistan. Electronic address: Drshakeel@szabmu.edu.pk.
  • Muhammad Faheemuddin
    Department of Prosthodontics and Dental Implantology, College of Dentistry, King Faisal University, Al-Ahsa, KSA. Electronic address: mnaseem@kfu.edu.sa.

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