Automatic Detection of Periapical Osteolytic Lesions on Cone-beam Computed Tomography Using Deep Convolutional Neuronal Networks.

Journal: Journal of endodontics
PMID: 35952897

Abstract

INTRODUCTION: Cone-beam computed tomography (CBCT) is an essential diagnostic tool in oral radiology. Radiolucent periapical lesions (PALs) represent the most frequent jaw lesions. However, the description, interpretation, and documentation of radiological findings, especially incidental findings, are time-consuming and resource-intensive, requiring a high degree of expertise. To improve quality, dentists may use artificial intelligence in the form of deep learning tools. This study was conducted to develop and validate a deep convolutional neuronal network for the automated detection of osteolytic PALs in CBCT data sets.

Authors

  • Barbara Kirnbauer
    Division of Oral Surgery and Orthodontics, Department of Dental Medicine and Oral Health, Medical University of Graz, Graz, Austria. Electronic address: barbara.kirnbauer@medunigraz.at.
  • Arnela Hadzic
    Institute for Computer Vision and Graphics, Graz University of Technology, Graz, Austria.
  • Norbert Jakse
    Division of Oral Surgery and Orthodontics, Department of Dental Medicine and Oral Health, Medical University of Graz, Graz, Austria.
  • Horst Bischof
    Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria.
  • Darko Štern
    b Institute for Computer Graphics and Vision, Graz University of Technology, BioTechMed , Graz , Austria , and.

Keywords

External Resources

Popular Topics
Recent Journals