A Deep Learning Model with High Standalone Performance for Diagnosis of Unruptured Intracranial Aneurysm.

Journal: Yonsei medical journal
Published Date: Nov 1, 2021

Abstract

PURPOSE: This study aimed to investigate whether a deep learning model for automated detection of unruptured intracranial aneurysms on time-of-flight (TOF) magnetic resonance angiography (MRA) can achieve a target diagnostic performance comparable to that of human radiologists for approval from the Korean Ministry of Food and Drug Safety as an artificial intelligence-applied software.

Authors

  • Bio Joo
    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.
  • Hyun Seok Choi
    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.
  • Sung Soo Ahn
    Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea. sungsoo@yuhs.ac.
  • Jihoon Cha
    Department of Radiology Yonsei University Medical Center Yonsei University College of Medicine Seoul Republic of Korea.
  • 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.
  • 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.
  • Hwiyoung Kim
    Department of Radiological Science, Yonsei University College of Medicine, Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea. Electronic address: HYKIM82@yuhs.ac.
  • Kyunghwa Han
    From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea (S.H.P.); and Department of Radiology, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.).
  • Hwa Pyung Kim
  • Jong Mun Choi
    DEEPNOID, Seoul, Korea.
  • Sang Min Lee
    Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Tae Gyu Kim
    DEEPNOID, Seoul, 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.

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