Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals
Phase: NA
Conditions: Acute Ischemic Stroke (AIS)
Sponsor: The University of Texas Health Science Center, Houston

IRB NUMBER: HSC-MS-19-0630 
IRB APPROVAL DATE: 02/09/2023 
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Deep Learning Enabled Endovascular Stroke Therapy Screening 
in Community Hospitals 
 
NCT05838456 
 
Version Date: 02/09/2023 
 
 

IRB NUMBER: HSC-MS-19-0630 
IRB APPROVAL DATE: 02/09/2023 
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Protocol Title: 
Deep Learning Enabled Endovascular Stroke Therapy Screening 
in Community Hospitals 
 
Protocol Version/Date: 
Principal Investigator: 
3.0, 01/16/2023 
 
Sunil A. Sheth, MD, Luca Giancardo, PhD 
 
Co-Investigators: 
Youngran Kim, MD 
 
Key Study Personnel: 
Sergio A. Salazar Marioni 
 
Population: 
5,000 subjects 
 
Number of Sites: 
4 Sites 
Memorial Hermann Hospital – TMC 
Memorial Hermann Hospital – Memorial City 
Memorial Hermann Hospital – Southwest 
Memorial Hermann Hospital – Woodlands 
 
Study Duration: 
July 15th 2019 – March 01st, 2026 
Subject Duration: 
Not applicable 
 
 
Acute Ischemic Stroke (AIS) is the leading cause of long-term disability in the US, and 
every minute of delay from onset to treatment reduces the likelihood of good clinical outcome. As 
a result of our ongoing Comprehensive Stroke Center (CSC) quality improvement efforts, we have 
identified a key delay: time from the completion of computed tomography (CT) angiography of 
the head and neck to interpretation in the setting of AIS care. Motivated by this unmet need in 
clinical care, and in partnership with the UTHealth School of Biomedical Informatics, we recently 
developed a machine-learning algorithm to automate the interpretation of CT angiography (CTA) 
for the presence or absence of large vessel occlusion (LVO) and other stroke relevant information 
such as estimated stroke core size. We propose a prospective, cluster- randomized study to 
determine whether incorporating our automated machine learning (ML) algorithms to AIS care pathways 
reduces the time from patient ER arrival to imaging interpretation by sending automated alerts of 
its findings to physicians on the stroke care team. Physicians will ultimately decide if patients are 
eligible for thrombectomy, and all patients will receive the standard of care following current 
guidelines. This strategy is expected to help improve patient stratification procedures for 
endovascular thrombectomy by using commonly available data. Our unique research team 
consisting of machine-learning and AIS imaging experts will take advantage of the rich and diverse 
talents across the UTHealth Campuses and Memorial Hermann Hospital System. 

IRB NUMBER: HSC-MS-19-0630 
IRB APPROVAL DATE: 02/09/2023 
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Background Information 
 
Acute Ischemic Stroke (AIS) is the leading cause of long-term disability in the US adult population 
with nearly 800,000 new patients affected by stroke annually in the US, and with Large Vessel Occlusion 
(LVO), a subtype of AIS that could require endovascular stroke therapy (EST), disproportionately 
contributing to stroke-related dependence and death.1,2 The last four years, however, have borne witness to 
rapid changes in care for patients with LVO AIS. In 2015, five randomized clinical trials established EST 
as one of the most significant advances in modern medicine.3-7 Compared to patients treated with medical 
management alone, LVO AIS patients treated with EST were nearly 2.5 fold more likely to achieve good 
neurological outcomes (number needed to treat = 3), a treatment effect size rivaling that of antibiotics to 
treat acute bacterial infections.8,9 Yet these advances have revealed shortcomings in our AIS systems of 
care, as we are now faced with a great number of patients that must be triaged with an increasingly complex 
evaluation that must be performed in a shorter time. 
 
A known dogma is that time is brain in AIS. Every minute after an occlusion of the cerebral 
vasculature 2 million neurons die, and key quality metrics in AIS revolve around reducing the time from 
when the patient arrives to the hospital to when treatment is initiated.13 While we now know from the results 
of pivotal randomized clinical trials that EST, which re-opens occluded cerebral vessels through catheter- 
based procedures, can dramatically improve patient outcomes following AIS, these outcomes are still highly 
time-dependent. 10,11 This challenge is particularly relevant in the non-Texas Medical Center MH campuses, 
where delays after imaging arise for two reasons. First, Neurologists caring for patients in these settings are 
not comfortable interpreting the imaging studies to determine the presence or absence of LVO without 
relying on a Radiologist interpretation, thereby adding an additional step to the care pathway. Second, for 
Neurologists evaluating these patients remotely through Telemedicine, the imaging studies may not be 
completed at the time of the initial evaluation, and the requirement for the physician to remember to check 
periodically to see when the imaging study is complete can lead to fall outs, particularly when they may 
have moved on to care for another patient. 
Through our continuous process of quality improvement, we recently conducted an analysis of 
delays in AIS treatment. Among 833 patients with AIS, median time from ER arrival to CTA interpretation 
for presence or absence of LVO was 65 minutes [IQR 41 - 127]. In order to comply with our Joint 
Commission requirements for Comprehensive Stroke Center, the EST procedure must be initiated within 
90 minutes from patient arrival to the ER, and given the multiple steps that must occur after the imaging 
result and before EST initiation, this time interval from ER arrival to imaging result was identified as a 
weak point in the process. 
Motivated by this unmet need in clinical care, and in partnership with the UTHealth School of 
Biomedical Informatics, we recently developed a machine-learning algorithm to automate the interpretation 
of CTA for the presence or absence of LVO. This novel deep neural network architecture, named 
DeepSymNet, was trained on over 400 CTA studies, using the radiology report and expert readers 
interpretations as gold standard, and can nearly instantaneously analyze images for LVO.12 We then 
validated this algorithm on a fully external dataset of 126 patients, and found that it can perform this task 
with very high discrimination (Figure 1). 

IRB NUMBER: HSC-MS-19-0630 
IRB APPROVAL DATE: 02/09/2023 
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Given these results, we propose a prospective, cluster-randomized study to determine whether AIS 
care pathways incorporating image based ML algorithms reduces the time from patient ER arrival to 
imaging interpretation (Figure 2). Our unique research team consisting of machine-learning and AIS imaging 
experts will take advantage of the rich and diverse talents across the UTHealth Campuses and the Memorial 
Hermann Hospital System. Given our preliminary findings, we hypothesize that integration of these 
approaches into the AIS care pathway will reduce delays from CTA acquisition to interpretation, without 
requiring any change in existing CT hardware or acquisition protocols. 
Objectives 
 
The expected outcome is the creation of a state-of-the-art infrastructure for deploying artificial 
intelligence algorithms in the clinical practice and a thorough evaluation of a potential paradigm shifting 
approach for a measurable and sustainable quality improvement of stroke care. 
Study Design 
 
To test the previously mentioned hypotheses, we propose a multi-center, stepped wedge cluster- 
randomized study that will include patients across 4 hospitals in the Memorial Hermann/UTHealth stroke 
system of care in the greater Houston area. The software will be randomly enabled at each of 4 
comprehensive stroke center hospitals to evaluate the impact of this software on primary and secondary 
outcomes. The primary outcome will be the time from patient ER arrival to initiation of EST (“door-to- 
groin” time). Secondary outcomes include the number of patients treated with EST and 90-day disability 
outcomes. 
 
Study Population 
 
The alert system will be enabled for all CT angiography imaging studies for participating Hospitals. 
A valid sample will be one that satisfy the following criteria: 
 
Inclusion Criteria 
1. 
Male or Female, 18 years of age or older. 
2. 
Patients who present to the emergency department with signs and/or symptoms concerning for 
acute ischemic stroke. 
3. 
Patients who undergo CT angiography imaging 
4. 
Patients determined to have a large vessel occlusion acute ischemic stroke. This determination will be made 
based on official radiology report for the CT angiography imaging. 

IRB NUMBER: HSC-MS-19-0630 
IRB APPROVAL DATE: 02/09/2023 
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Exclusion Criteria 
1. Patients with incomplete data on the electronic medical record. 
 
Study Procedures 
 
ML Algorithms Integration 
 
Custom ML software (Viz.AI) will be installed on a server and receive data from the centralized 
PACS system. This software has been purchased by the MH hospital system for commercial use. The 
system will then send alerts to the care team through notifications pushed through the Viz.AI app present 
on the physician, nurse and technologist’s mobile phone. Diagnosis and treatment decisions will be based 
on the clinical evaluation and review of the images by the treating physician, as per routine standard of 
care. This study will not interfere with routine clinical work. Data capture will be performed after treatment 
decisions and care have been delivered. 
 
Clinical Data Acquisition 
 
Clinical data will be collected at each of the participating sites as a part of our ongoing 
Comprehensive Stroke System quality improvement protocols. All other clinical and outcome data will be 
collected through electronic medical record review. Captured data will include patient demographics, past 
medical history, imaging utilization and imaging findings, time intervals (i.e. time from last known well to 
presentation, time from door to imaging, time from door to IV tPA, time from door to groin puncture), 
procedural outcomes (i.e. TICI scores, hemorrhage rates), discharge clinical outcomes, and in patients who 
received EST or IV tPA, 90-day disability outcomes. These data are routinely captured per standard-of-care 
at our Comprehensive Stroke System and will be used for analysis as a part of this study. 
Ethics 
 
Being an observational project, there will be no interventions, investigations, tests, drugs, and/or 
interviews administered to patients. We do not expect any research-related adverse events. All personal 
health information from patients will be kept confidential and in compliance with HIPAA regulations. This 
site is seeking waver of consent for this observational study involving electronic medical chart review and 
analysis of de-identified CTA images. 
Risks and benefits for the human subjects 
 
Risks 
In this study, ML algorithms are not used for diagnosis, cure, mitigation, treatment or prevention as it 
only sends alerts. All diagnosis and treatment decisions are still based on the clinical evaluations and 
radiology reports. As such, we do not foresee that this study poses more than minimal risk encountered 
during the routine stroke care. 
 
Benefits 
We do not foresee any immediate benefits for the human subjects involved. However, if successful, 
this study could enable a faster time-to-treatment for stroke patients. 
 
Data handling and record keeping 

IRB APPROVAL DATE: 02/09/2023 
 
Imaging Data 
The server running the ML algorithm will hosted by Viz.AI. This server and imaging transfer protocol have 
been reviewed by MH IT and found to fall within acceptable security parameters. The Viz.AI software is a 
commercial product, and its usage in this study will fall within the boundaries of its intended use as a 
commercial product and the contract with MH hospital. 
 
Clinical Data 
All the clinical numerical variables collected for this study will be anonymized and stored in a central 
RedCap database inside the UTHealth network. All users of this database will comply with HIPAA 
regulations and with Good Clinical Practice for the handling of electronic clinical data. Only the study key 
personnel will have access to the project data. 
Each subject will be given a unique subject identification which will be linked to the subject’s MRN 
for prospective data collection. If data is to be analyzed, no patient identifier will be included to ensure no 
patient can be tracked or identified. Thus, analysis will solely be performed on de-identified data. No paper 
records will be kept. To ensure reproducibility of the finding, the UTHealth RedCap will continue to store 
the data after the completion of the study and until IT resources will allow to maintain the machine updated 
and protected. If this is not possible, all the data containing Protected Health Information (PHI) will be 
destroyed. 
 
Data Sharing 
In order to allow for a broader reach for our finding and scientific publications, we might have to share part 
of the data for scientific non-commercial purposes. In this case, we will strip the imaging and clinical data 
of any PHI information. 
 
Publication Plan 
 
Results based on data gathered from this this study may serve to elaborate abstract submissions or 
manuscripts for peer-reviewed journals. Research publications will not include any patient-specific 
information or identifiers. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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IRB NUMBER: HSC-MS-19-0630 

IRB NUMBER: HSC-MS-19-0630 
IRB APPROVAL DATE: 02/09/2023 
 
 
 
 
 
References 
 
1. 
Benjamin, E.J. et al. Heart Disease and Stroke Statistics-2018 Update: A Report From the American 
Heart Association. Circulation 137, e67–e492 (2018). 
2. 
Malhotra, K., Gornbein, J. & Saver, J. L. Ischemic Strokes Due to Large-Vessel Occlusions 
Contribute Disproportionately to Stroke-Related Dependence and Death: A Review. Front Neurol 
8, 651 (2017). 
3. 
Berkhemer, O. A. et al. A randomized trial of intraarterial treatment for acute ischemic stroke. N. 
Engl. J. Med. 372, 11–20 (2015). 
4. 
Campbell, B. C. V. et al. Endovascular therapy for ischemic stroke with perfusion-imaging 
selection. N. Engl. J. Med. 372, 1009–1018 (2015). 
5. 
Goyal, M. et al. Randomized assessment of rapid endovascular treatment of ischemic stroke. N. 
Engl. J. Med. 372, 1019–1030 (2015). 
6. 
Saver, J. L. et al. Stent-retriever thrombectomy after intravenous t-PA vs. t-PA alone in stroke. N. 
Engl. J. Med. 372, 2285–2295 (2015). 
7. 
Jovin, T. G. et al. Thrombectomy within 8 Hours after Symptom Onset in Ischemic Stroke. N. Engl. 
J. Med. 150417035025009 (2015). doi:10.1056/NEJMoa1503780 
8. 
Tibayrenc M. Encyclopedia of Infectious Diseases: Modern Methodologies. John Wiley & Sons; 
2007. 
9. 
Powers, W. J. et al. 2018 Guidelines for the Early Management of Patients With Acute Ischemic 
Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American 
Stroke Association. Stroke; a journal of cerebral circulation 49, e46–e110 (2018). 
10. 
Sheth, S. A. et al. Time to endovascular reperfusion and degree of disability in acute stroke. Ann 
Neurol 78, 584–593 (2015). 
11. 
Saver, J. L. et al. Time to Treatment With Endovascular Thrombectomy and Outcomes From 
Ischemic Stroke: A Meta-analysis. JAMA 316, 1279–1288 (2016). 
12. 
Barman, A. et al. Determining Ischemic Stroke From CT-Angiography Imaging Using Symmetry- 
Sensitive Convolutional Networks. IEEE International Symposium on Biomedical Imaging ISBI 
(2019) (in print) 
13. 
Saver, J. L. Time is brain--quantified. Stroke; a journal of cerebral circulation 37, 263–266 
(2006). 

IRB NUMBER: HSC-MS-19-0630 
IRB APPROVAL DATE: 02/09/2023 
 
 
 
 
 
 
Figure 1. DeepSymNet performance for large vessel occlusion detection in fully external dataset of acute ischemic 
stroke from the clinical PACS at Memorial Hermann/UTHealth. The model was previously trained on an independent 
set of 297 CTA images. AUC: Area Under the Receiving Operating Characteristic curve. 
 
Figure 2. Overview of current stroke care and the proposed approach, designed to reduce the time to imaging 
interpretation