- Deep Learning of Time–Signal Intensity Curves from Dynamic Susceptibility Contrast Imaging Enables Tissue Labeling and Prediction of Survival in Glioblastoma
The autoencoder perfusion pattern analysis enabled tissue characterization of peritumoral areas, providing heterogeneity and dynamic information that may provide useful prognostic information in IDH wild-type glioblastoma.
- An Artificial Intelligence Tool for Clinical Decision Support and Protocol Selection for Brain MRI
This model achieved high accuracy on a standard based on physician consensus. It showed promise as a clinical decision support tool to reduce the workload by automating the protocolling of a sizeable portion of examinations while maintaining high accuracy.
- Automated Estimation of Quantitative Lesion Water Uptake as a Prognostic Biomarker for Patients with Ischemic Stroke and Large-Vessel Occlusion
ASPECTS-net water uptake could independently predict 90-day neurologic outcomes in patients with acute ischemic stroke and large-vessel occlusion.
- Viz.ai Implementation of Stroke Augmented Intelligence and Communications Platform to Improve Indicators and Outcomes for a Comprehensive Stroke Center and Network
There was an immediate improvement following Viz.ai implementation for both direct-arriving and telemedicine-transfer thrombectomy cases.
- Automated Detection of Cerebral Aneurysms on TOF-MRA Using a Deep Learning Approach: An External Validation Study
The software was highly reliable in detecting saccular aneurysms, while for fusiform or thrombosed aneurysms, further improvements are needed.
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