DEEP LEARNING-DRIVEN SEGMENTATION OF DENTAL IMPLANTS AND PERI-IMPLANTITIS DETECTION IN ORTHOPANTOMOGRAPHS: A NOVEL DIAGNOSTIC TOOL.

Journal: The journal of evidence-based dental practice
PMID: 39947781

Abstract

INTRODUCTION AND OBJECTIVE: Dental implants are well-established for restoring partial or complete tooth loss, with osseointegration being essential for their long-term success. Peri-implantitis, marked by inflammation and bone loss, compromises implant longevity. Current diagnostic methods for peri-implantitis face challenges such as subjective interpretation and time consumption. Our deep learning-based approach aims to address these limitations by providing a more accurate and efficient solution. This study aims to develop a deep learning-based approach for segmenting dental implants and detecting peri-implantitis in orthopantomographs (OPGs), enhancing diagnostic accuracy and efficiency.

Authors

  • Erdoğan Kibcak
    Near East University, Department of Oral and Maxillofacial Surgery, Nicosia, Cyprus.
  • Oğuz Buhara
    Near East University, Department of Oral and Maxillofacial Surgery, Nicosia, Cyprus.
  • Ali Temelci
    Private Practice, Kyrenia, Cyprus.
  • Nurullah Akkaya
    Department of Computer Engineering, Applied Artificial Intelligence Research Centre, Near East University, Lefkosa, Northern Cyprus, Mersin 10, Turkey.
  • Gürkan Ünsal
    Faculty of Dentistry, Department of Dentomaxillofacial Radiology, Near East University, Nicosia, Cyprus.
  • Giuseppe Minervini
    Saveetha Dental College & Hospitals, Saveetha Institute of Medical & Technical Sciences, Saveetha University, Chennai, India. Giuseppe.minervini@unicampania.it.

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