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- Title
A Deep Learning Framework for Deriving Noninvasive Intracranial Pressure Waveforms from Transcranial Doppler.
- Authors
Megjhani, Murad; Terilli, Kalijah; Weinerman, Bennett; Nametz, Daniel; Kwon, Soon Bin; Velazquez, Angela; Ghoshal, Shivani; Roh, David J.; Agarwal, Sachin; Connolly, E. Sander; Claassen, Jan; Park, Soojin
- 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
- Subjects
INTRACRANIAL pressure; DEEP learning; CEREBRAL circulation; FLOW velocity; TRANSFORMER models
- Publication
Annals of Neurology, 2023, Vol 94, Issue 1, p196
- ISSN
0364-5134
- Publication type
Article
- DOI
10.1002/ana.26682