Automated analysis of three-dimensional CBCT images taken in natural head position that combines facial profile processing and multiple deep-learning models.

Journal: Computer methods and programs in biomedicine
Published Date: Sep 9, 2022

Abstract

BACKGROUND AND OBJECTIVES: Analyzing three-dimensional cone beam computed tomography (CBCT) images has become an indispensable procedure for diagnosis and treatment planning of orthodontic patients. Artificial intelligence, especially deep-learning techniques for analyzing image data, shows great potential for medical and dental image analysis and diagnosis. To explore the feasibility of automating measurement of 13 geometric parameters from three-dimensional cone beam computed tomography images taken in natural head position (NHP), this study proposed a smart system that combined a facial profile analysis algorithm with deep-learning models.

Authors

  • Janghoon Ahn
    Department of Orthodontics, Kangnam Sacred Heart Hospital, Hallym University, Singil-ro 1 gil, Yeongdeungpo-gu, Seoul 07441, Republic of Korea.
  • Thong Phi Nguyen
    Department of Mechanical Design Engineering, Hanyang University, 222, Wangsimni-ro, Seongdongsu, Seoul, 04763, Republic of Korea.
  • Yoon-Ji Kim
    Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Kangwon-do, 26493, South Korea.
  • Taeyong Kim
    Department of Civil and Environmental Engineering, Seoul National University, Seoul, Republic of Korea.
  • Jonghun Yoon
    Department of Mechanical Engineering, Hanyang University, 55, Hanyangdaehak-ro, Sangnok-gu, Ansan-si, Gyeonggi-do, 15588, Republic of Korea. Electronic address: yooncsmd@gmail.com.

Keywords

External Resources

Popular Topics
Recent Journals