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

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This study introduces a novel deep learning framework for noninvasive intracranial pressure (ICP) monitoring. The new method accurately estimates ICP using blood pressure and other signals, improving patient care in neurocritical settings.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Neurocritical Care

Background:

  • Elevated intracranial pressure (ICP) is a critical concern in neurocritical care, leading to significant disability and mortality.
  • Current ICP monitoring techniques are invasive, posing risks and limitations for patient management.

Purpose of the Study:

  • To develop and validate a noninvasive deep learning framework for accurate ICP estimation.
  • To improve upon existing methods for ICP monitoring in neurocritical care patients.

Main Methods:

  • A deep learning framework utilizing a domain adversarial neural network (DANN) was designed.
  • The model integrates data from blood pressure, electrocardiogram, and cerebral blood flow velocity for ICP estimation.
  • Performance was evaluated against nonlinear approaches like support vector regression.

Main Results:

  • The DANN model achieved a mean of median absolute error of 3.88 ± 3.26 mmHg.
  • Domain adversarial transformers demonstrated a similar accuracy with 3.94 ± 1.71 mmHg error.
  • The proposed framework showed a 26.7% and 25.7% lower error compared to support vector regression.

Conclusions:

  • The developed deep learning framework offers a more accurate noninvasive method for estimating ICP.
  • This noninvasive approach has the potential to enhance patient outcomes and reduce risks associated with invasive monitoring.