Diagnostic Accuracy of a Deep Learning Algorithm for Detecting Unruptured Intracranial Aneurysms in Magnetic Resonance Angiography: A Multicenter Pivotal Trial.

Journal: World neurosurgery
Published Date: Mar 12, 2025

Abstract

BACKGROUND: Intracranial aneurysm rupture is associated with high mortality and disability rates. Early detection is crucial, but increasing diagnostic workloads place significant strain on radiologists. We evaluated the efficacy of a deep learning algorithm in detecting unruptured intracranial aneurysms (UIAs) using time-of-flight (TOF) magnetic resonance angiography (MRA).

Authors

  • Wi-Sun Ryu
    Artificial Intelligence Research Center, JLK Inc., 5 Teheran-ro 33-gil, Seoul, Republic of Korea. wisunryu@gmail.com.
  • Sungmoon Jeong
    Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, South Korea.
  • Jaechan Park
    Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, South Korea. jparkmd@hotmail.com.
  • Dougho Park
    Department of Rehabilitation Medicine, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea.
  • Heeyoung Kim
    Institute of Information Technology, Kwangwoon University, Seoul, Republic of Korea.
  • Myungjae Lee
    JLK, Incorporated, Eonju-ro, Gangnam-gu, Seoul, South Korea.
  • Dongmin Kim
    JLK, Incorporated, Eonju-ro, Gangnam-gu, Seoul, South Korea.
  • Myungsoo Kim
    Department of Neurosurgery, School of Medicine of Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41944, South Korea.
  • Byoung-Joon Kim
    Department of Neurosurgery, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
  • Hui Joong Lee
    Department of Radiology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.

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