Accuracy and clinical validity of automated cephalometric analysis using convolutional neural networks.

Journal: Orthodontics & craniofacial research
PMID: 37326233

Abstract

BACKGROUND: This study aimed to assess the error range of cephalometric measurements based on the landmarks detected using cascaded CNNs and determine how horizontal and vertical positional errors of individual landmarks affect lateral cephalometric measurements.

Authors

  • Seyun Kang
    Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Inhwan Kim
    Department of computer science, Sangmyung University, Seoul, South Korea.
  • Yoon-Ji Kim
    Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Kangwon-do, 26493, South Korea.
  • Namkug Kim
    Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Seung-Hak Baek
    Department of Orthodontics.
  • Sang-Jin Sung
    Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

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