# A Machine Learning Approach to Classifying Time Since Stroke using Medical Imaging

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $429,902

## Abstract

PROJECT SUMMARY/ABSTRACT
Stroke is a leading cause of mortality and morbidity in the United States, with approximately 795,000
Americans experiencing a new or recurrent stroke each year. Intravenous tissue plasminogen activator (IV
tPA) is the dominant and most proven treatment option, but its use is only indicated within 4.5 hours following a
stroke. Unfortunately, up to 30% of stroke patients present with an unknown time since stroke (TSS) symptom
onset, which makes them ineligible to receive IV tPA. Many of these individuals could be spared severe
morbidity or mortality if there existed an alternative method for establishing TSS, allowing them to be identified
and treated. This proposal will develop machine learning methods to create a physiologically grounded method
for predicting TSS based on multiparametric magnetic resonance (MR) and computed tomography (CT)
imaging data. We believe our proposed techniques will outperform state-of-the-art methods that are based on
subjective image interpretation, and have the potential to provide an objective data point that may be used in
conjunction with the subjective assessments of experts, or in clinical environments that lack expertise in stroke
imaging
Research has established that MR and CT imaging captures information that correlates with TSS. However,
existing methods for extracting this information are based on a physician subjectively interpreting the images
and delineating regions of interest, processes that have been documented to have only weak to moderate
agreement across trained expert reviewers. An automated approach that comprehensively analyzes the
spectrum of imaging data could identify complex relationships across channels that more accurately classify
TSS. For example, in MR, diffusion-weighted, perfusion-weighted, and fluid attenuated inversion recovery
imaging all play important roles in characterizing a stroke, but a deep understanding of how each channel may
be combined to describe TSS is unknown. We propose to establish new deep learning methods for fusing this
information. Specifically, we will: 1) develop a machine learning framework for classifying TSS; 2) develop a
deep convolutional autoencoder to generate novel multimodal image representations from MR and CT to
improve classification; and 3) implement visualization techniques that elucidate the relationship between deep
features and pathophysiological stroke processes. Under this project, we will use data from the UCLA and UCI
Stroke Centers, allowing us to study different patient populations and imaging techniques. The successful
completion of this research will provide a new method for estimating TSS from imaging, leading to new
prospective trials for providing therapy to patients with unknown TSS.

## Key facts

- **NIH application ID:** 10109154
- **Project number:** 5R01NS100806-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Corey Wells Arnold
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $429,902
- **Award type:** 5
- **Project period:** 2018-05-01 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10109154

## Citation

> US National Institutes of Health, RePORTER application 10109154, A Machine Learning Approach to Classifying Time Since Stroke using Medical Imaging (5R01NS100806-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10109154. Licensed CC0.

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