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

Ann Neurol. 2023 Jul;94(1):196-202. doi: 10.1002/ana.26682. Epub 2023 Jun 1.

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Blood Pressure / physiology
  • Cerebrovascular Circulation / physiology
  • Deep Learning*
  • Humans
  • Intracranial Hypertension* / etiology
  • Intracranial Pressure / physiology
  • Ultrasonography, Doppler, Transcranial / adverse effects