A Deep Learning Framework for Deriving Noninvasive Intracranial Pressure Waveforms from Transcranial Doppler.

Journal: Annals of neurology
PMID:

Abstract

Increased intracranial pressure (ICP) causes disability and mortality in the neurointensive care population. Current methods for monitoring ICP are invasive. We designed a deep learning framework using a domain adversarial neural network to estimate noninvasive ICP, from blood pressure, electrocardiogram, and cerebral blood flow velocity. Our model had a mean of median absolute error of 3.88 ± 3.26 mmHg for the domain adversarial neural network, and 3.94 ± 1.71 mmHg for the domain adversarial transformers. Compared with nonlinear approaches, such as support vector regression, this was 26.7% and 25.7% lower. Our proposed framework provides more accurate noninvasive ICP estimates than currently available. ANN NEUROL 2023;94:196-202.

Authors

  • Murad Megjhani
    Department of Neurology, Columbia University, New York, NY, USA.
  • Kalijah Terilli
  • Bennett Weinerman
    Program for Hospital and Intensive Care Informatics, Department of Neurology, Columbia University, New York, NY, USA.
  • Daniel Nametz
    Department of Neurology, Columbia University, New York, NY, USA.
  • Soon Bin Kwon
    Department of Neurology, Columbia University, New York, NY, USA.
  • Angela Velazquez
    Department of Neurology, Columbia University, New York, NY, USA.
  • Shivani Ghoshal
    Department of Neurology, Columbia University, New York, NY, USA.
  • David J Roh
    Department of Neurology, Columbia University, New York, NY, USA.
  • Sachin Agarwal
    Department of Neurology, Columbia University, New York, NY, USA.
  • E Sander Connolly
    Department of Neurosurgery, Columbia University, College of Physicians and Surgeons, New York, USA.
  • Jan Claassen
    Department of Neurology, Columbia University, New York, NY, USA.
  • Soojin Park
    Department of Neurology, Department of Biomedical Informatics, Columbia University, New York, NY, USA.