Development and Multicenter, Multiprotocol Validation of Neural Network for Aberrant Right Subclavian Artery Detection.

Journal: Yonsei medical journal
PMID: 39193761

Abstract

PURPOSE: This study aimed to develop and validate a convolutional neural network (CNN) that automatically detects an aberrant right subclavian artery (ARSA) on preoperative computed tomography (CT) for thyroid cancer evaluation.

Authors

  • So Yeon Won
    Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea.
  • Ilah Shin
    Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University, College of Medicine, Seoul, Republic of Korea.
  • Eung Yeop Kim
    Department of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea.
  • Seung-Koo Lee
    Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, South Korea.
  • Youngno Yoon
    Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
  • Beomseok Sohn
    Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.

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