- 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.
- 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.
- 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.
- Cellular Density in Adult Glioma, Estimated with MR Imaging Data and a Machine Learning Algorithm, Has Prognostic Power Approaching World Health Organization Histologic Grading in a Cohort of 1181 Patients
Image-based estimation of glioma cellularity is a promising biomarker for predicting survival, approaching the prognostic power of World Health Organization grading, with added values of early availability, low risk, and low cost.
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