# Predicting Tissue and Functional Outcome in Acute Stroke

> **NIH NIH R01** · STANFORD UNIVERSITY · 2023 · $628,218

## Abstract

Abstract
Stroke is a disabling cerebrovascular disease that causes 5.5 million deaths each
year globally. The disease progresses rapidly and irreversibly, leaving a narrow
time window for intervention. Existing methods for patient selection for endo-
vascular thrombectomy are suboptimal, based exclusively on simple linear
threshold models applied to neuroimaging. Deep learning has shown great
promise in recent years for many medical applications. We believe that it can
be used to integrate imaging and non-imaging data in a seamless and data-
driven way to improve stroke triage and clinical trials.
The goal of this project is to develop deep convolutional neural network
approaches to the initial MR and CT imaging, the most commonly performed
stroke imaging protocol in acute ischemic stroke patients, and to combine this
with non-imaging clinical information. We will train networks to predict the
most likely final tissue and clinical outcomes under 2 extreme conditions
(major reperfusion and minimal reperfusion) to estimate the treatment effect at
the individual level. Next, we use the methods and learning from this first study
to train deep learning models without using contrast perfusion imaging, which
will improve safety, cost, and time-to-treatment. Finally, we will test the
generalizability and explainability of these AI methods in external cohorts
which differ in terms of population and scanner types, including testing on data
from mobile CT scanners.
Accomplishment of these aims will fundamentally shift the acute stroke
paradigm beyond the relatively simplistic mismatch concept and replace it with
a data-driven method that takes into account the immense amount of imaging
and clinical data that can be brought to the stroke decision-making process.
The methods developed will improve long-term outcomes and reduce of the
cost of stroke care worldwide.

## Key facts

- **NIH application ID:** 10568740
- **Project number:** 1R01NS130172-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Gregory George Zaharchuk
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $628,218
- **Award type:** 1
- **Project period:** 2023-08-15 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10568740

## Citation

> US National Institutes of Health, RePORTER application 10568740, Predicting Tissue and Functional Outcome in Acute Stroke (1R01NS130172-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10568740. Licensed CC0.

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