DeepSAP: A Novel Brain Image-Based Deep Learning Model for Predicting Stroke-Associated Pneumonia From Spontaneous Intracerebral Hemorrhage.

Journal: Academic radiology
Published Date: Jul 2, 2024

Abstract

RATIONALE AND OBJECTIVE: Stroke-associated pneumonia (SAP) often appears as a complication following intracerebral hemorrhage (ICH), leading to poor prognosis and increased mortality rates. Previous studies have typically developed prediction models based on clinical data alone, without considering that ICH patients often undergo CT scans immediately upon admission. As a result, these models are subjective and lack real-time applicability, with low accuracy that does not meet clinical needs. Therefore, there is an urgent need for a quick and reliable model to timely predict SAP.

Authors

  • Xu Qiao
    School of Control Science and Engineering Shandong University Jinan Shandong China.
  • Chenyang Lu
    Department of Computer Science and Engineering, Washington University, St. Louis, MO.
  • Min Xu
    Department of Gastroenterology, Shanghai First People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
  • Guangtong Yang
    College of Medicine and Biomedical Information Engineering, Northeastern University, 110004 Shenyang, China.
  • Wei Chen
    Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.
  • Zhiping Liu
    Pinggu District Center for Disease Control and Prevention, Beijing 101200, China.

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