# Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2021 · $481,432

## Abstract

Project Summary/Abstract
Stroke is the 5th leading cause of death in the United States. Endovascular stroke therapy (EST) has
revolutionized the management of large vessel occlusion (LVO) acute ischemic stroke (AIS), which accounts
for a disproportionate amount of disability in stroke. While this therapy has been shown to significantly improve
clinical outcomes in multiple clinical trials, these studies nearly all required screening patients with advanced
NeuroImaging such as CT Perfusion (CTP), a modality not available to the majority of community hospitals. As
such, there is a pressing need to for a tool able to identify EST candidates leveraging the infrastructure already
existing in community hospitals. We envision a software-based service to automate the NeuroImaging
evaluation for EST using CT angiography (CTA). We developed and tested a prototype of a novel deep neural
network architecture called DeepSymNet. Our preliminary data indicate that uniquely using CTAs we can
determine (1) the presence or absence of a large vessel occlusion (2) if the extent of ischemic core and (3)
volume of tissue “at risk” (penumbra) is above or below the thresholds used in the clinical trials, when
compared to concurrently obtained results using CTP.
We will pursue our project goal with three aims:
- Aim 1 - Establish one of the largest multi-institution dataset for neuro-imaging research in acute ischemic
stroke. We will acquire a multi-center dataset including imaging and clinical data from 15 hospitals across
Texas and California, from a range of scanners, imaging acquisition protocols, and hospital types (i.e. large
academic and smaller community).
- Aim 2 - Develop interpretable deep learning models to determine the eligibility for EST. We will methodically
test a set of model architectures, data augmentation strategies, loss functions and pre-processing steps based
on DeepSymNet. We will train and test the algorithm against various definitions of infarct core and penumbral
volume based on CTP results. This approach will allow for models adaptable to the everchanging definition of
EST eligibility.
 – Aim 3 - Evaluate the external validity of DeepSymNet-based models on a large multi-center independent
dataset. To accomplish this aim, we will deploy our DeepSymNet software on patient imaging and data from
multiple hospitals, which were not used in the creation of the software. We will also validate our approach of
using CTA alone to determine ischemic core by validating blinded reads of infarct core from CTA source
images performed by expert readers against concurrently acquired CTP results.
Completion of these aims will have a sustained, transformative impact by supporting the creation and
validation of decision support tools readily translatable to the patient bedside in the vast majority of community
hospitals across the country. In doing so, we hope to expand the access to high-quality EST screening to
thousands of additional AIS patients.

## Key facts

- **NIH application ID:** 10184809
- **Project number:** 1R01NS121154-01
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** LUCA GIANCARDO
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $481,432
- **Award type:** 1
- **Project period:** 2021-04-01 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10184809

## Citation

> US National Institutes of Health, RePORTER application 10184809, Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals (1R01NS121154-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10184809. Licensed CC0.

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