A stroke detection and discrimination framework using broadband microwave scattering on stochastic models with deep learning.

ABSTRACT: Stroke poses an immense public health burden and remains among the primary causes of death and disability worldwide. Emergent therapy is often precluded by late or indeterminate times of onset before initial clinical presentation. Rapid, mobile, safe and low-cost stroke detection technology remains a deeply unmet clinical need. Past studies have explored the use of microwave and other small form-factor strategies for rapid stroke detection; however, widespread clinical adoption remains unrealized. Here, we investigated the use of microwave scattering perturbations from ultra wide-band antenna arrays to learn dielectric signatures of disease. Two deep neural networks (DNNs) were used for: (1) stroke detection ("classification network"), and (2) characterization of the hemorrhage location an

SUBMITTER: Alon L 

PROVIDER: S-EPMC8688451 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature