[AI Seminar Series] Seminar by Prof. Kaveh Laksari, Friday January 24, 12-1pm, MRB Seminar Room

[Vassilis Tsotras]

The next talk at the AI Seminar Series, will be next Friday, January 24, 
12:00-1:00pm at the MRB Seminar Room (1st floor).

**** Pizza and refreshments will be provided ****

To keep track of the number of attendees, please *register* at:
https://www.eventbrite.com/e/ai-seminar-series-tickets-1210138078569

The talk will be given by Prof. Kaveh Laksari, Department of Mechanical 
Engineering, UCR

TITLE:
"End to end stroke triage using cerebrovascular morphology and machine 
learning"

ABSTRACT:
Rapid and accurate triage of acute ischemic stroke (AIS) is essential 
for early revascularization and improved patient outcomes. Response to 
acute reperfusion therapies varies significantly based on 
patient-specific cerebrovascular anatomy that governs cerebral blood 
flow. We present an end-to-end machine learning approach for automatic 
stroke triage. Employing a validated convolutional neural network (CNN) 
segmentation model for image processing, we extract each patient's 
cerebrovasculature and its morphological features from baseline 
non-invasive angiography scans. These features are used to detect 
occlusion's presence and the site automatically, and for the first time, 
to estimate collateral circulation without manual intervention. We then 
use the extracted cerebrovascular features along with commonly used 
clinical and imaging parameters to predict the 90 days functional 
outcome for each patient. The CNN model achieved a segmentation accuracy 
of 94% based on the Dice similarity coefficient (DSC). The automatic 
stroke detection algorithm had a sensitivity and specificity of 92% and 
94%, respectively. The models for occlusion site detection and automatic 
collateral grading reached 96% and 87.2% accuracy, respectively. 
Incorporating the automatically extracted cerebrovascular features 
significantly improved the 90 days outcome prediction accuracy. The 
fast, automatic, and comprehensive model presented here can improve 
stroke diagnosis, aid collateral assessment, and enhance prognostication 
for treatment decisions, using cerebrovascular morphology. We will also 
discuss physics-based deep learning novels methods to recover blood flow 
velocities in the brain vasculature using sparse clinical measurements.


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Sponsored by the RAISE at UCR Institute, the AI Seminar Series presents 
speakers working on cutting edge Foundational AI or apply AI in their 
research. The goal of these seminars is to inform the UCR community 
about current trends in AI research and promote collaborations between 
faculty in this emerging field.
These seminars are open to interested faculty and graduate students. 
Please forward this email to other colleagues or graduate students in 
your lab that may be interested. After the seminar a discussion will 
follow for questions, open problems, ideas for possible collaborations 
etc.

Sincerely,
Vassilis Tsotras
Professor, CSE Department
co-Director, RAISE at UCR Institute

Amit Roy-Chowdhury
Professor, ECE Department
co-Director, RAISE at UCR Institute