Automated Spontaneous Echo Contrast Detection Using a Multisequence Attention Convolutional Neural Network.

Journal: Ultrasound in medicine & biology
Published Date: Mar 8, 2024

Abstract

OBJECTIVE: Spontaneous echo contrast (SEC) is a vascular ultrasound finding associated with increased thromboembolism risk. However, identification requires expert determination and clinician time to report. We developed a deep learning model that can automatically identify SEC. Our model can be applied retrospectively without deviating from routine clinical practice. The retrospective nature of our model means future works could scan archival data to opportunistically correlate SEC findings with documented clinical outcomes.

Authors

  • Ouwen Huang
    Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
  • Zewei Shi
    Department of Biomedical Engineering, Duke University, Durham, NC, USA.
  • Naveen Garg
    Department of Abdominal Imaging, MD Anderson Cancer Center, Houston, TX, USA.
  • Corey Jensen
    Department of Abdominal Imaging, MD Anderson Cancer Center, Houston, TX, USA.
  • Mark L Palmeri
    Department of Biomedical Engineering, Duke University, Durham, NC, USA.

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