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

[Vassilis Tsotras]

Reminder for the AI Seminar tomorrow noon at the MRB seminar room.
Please register with the link below if you plan to attend.

thanks,
V. Tsotras


On 2025-01-19 20:26, Vassilis Tsotras via DataScience wrote:
> 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.
> 
> 
> ------------------------------------
> 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
> _______________________________________________