Using a Dual-Input Convolutional Neural Network for Automated Detection of Pediatric Supracondylar Fracture on Conventional Radiography.

Journal: Investigative radiology
PMID: 31725064

Abstract

OBJECTIVES: This study aimed to develop a dual-input convolutional neural network (CNN)-based deep-learning algorithm that utilizes both anteroposterior (AP) and lateral elbow radiographs for the automated detection of pediatric supracondylar fracture in conventional radiography, and assess its feasibility and diagnostic performance.

Authors

  • Jae Won Choi
  • Yeon Jin Cho
  • Seowoo Lee
    From the Department of Radiology, Seoul National University Hospital.
  • Jihyuk Lee
  • Seunghyun Lee
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA. Electronic address: seunghyun.lee.22@gmail.com.
  • Young Hun Choi
    Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Korea. iater@snu.ac.kr.
  • Jung-Eun Cheon
    Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Korea.
  • Ji Young Ha
    Department of Radiology, Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea.

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