Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT.

Journal: Journal of neurointerventional surgery
PMID: 37015781

Abstract

BACKGROUND: Automated measurement of the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) can support clinical decision making. Based on a deep learning algorithm, we developed an automated ASPECTS scoring system (Heuron ASPECTS) and validated its performance in a prespecified clinical trial.

Authors

  • Seong-Joon Lee
    Department of Neurology, Ajou University School of Medicine, Suwon, Gyeonggi-do, South Korea.
  • Gyuha Park
    Research Division, Heuron Co., Ltd, Incheon, South Korea.
  • Dohyun Kim
    Convergence Research Center for Diagnosis, Treatment, and Care of Dementia, Korea Institute of Science and Technology, Seoul, South Korea.
  • Sumin Jung
    Research Division, Heuron Co., Ltd, Incheon, South Korea.
  • Soohwa Song
    Heuron Co.,Ltd, Incheon, Republic of Korea, Incheon, Republic of Korea.
  • Ji Man Hong
    Department of Neurology, Ajou University School of Medicine, Suwon, Republic of Korea.
  • Dong Hoon Shin
    Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea.
  • Jin Soo Lee
    Department of Neurology, Ajou University Hospital, Ajou University School of Medicine, Suwon, Republic of Korea.

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