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HomeStroke: Vascular and Interventional NeurologyVol. 2, No. 5Artificial
Intelligence–Parallel Stroke Workflow Tool Improves Reperfusion Rates and
Door‐In to Puncture Interval
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ARTIFICIAL INTELLIGENCE–PARALLEL STROKE WORKFLOW TOOL IMPROVES REPERFUSION RATES
AND DOOR‐IN TO PUNCTURE INTERVAL

* Ameer E. Hassan
, DO,
* Victor M. Ringheanu
,
* Laurie Preston
and
* Wondwossen G. Tekle
, MD

Ameer E. Hassan
Ameer E. Hassan

*Correspondence to: Ameer E. Hassan, Department of Neurology, University of
Texas Rio Grande Valley School of Medicine, Edinburg, TX 78539. E‐mail:

E-mail Address: ameerehassan@gmail.com

https://orcid.org/0000-0002-7148-7616

, Department of Neurology, , University of Texas Rio Grande Valley School of
Medicine, , Edinburg, , TX,

, Department of Clinical Research, , Valley Baptist Medical Center, , Harlingen,
, TX,

, Neuroscience Department, , Valley Baptist Medical Center, , Harlingen, , TX,

Search for more papers by this author

,
Victor M. Ringheanu
Victor M. Ringheanu

https://orcid.org/0000-0002-5182-5489

, Department of Clinical Research, , Valley Baptist Medical Center, , Harlingen,
, TX,

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,
Laurie Preston
Laurie Preston

, Department of Clinical Research, , Valley Baptist Medical Center, , Harlingen,
, TX,

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and
Wondwossen G. Tekle
Wondwossen G. Tekle

https://orcid.org/0000-0001-5556-5699

, Department of Neurology, , University of Texas Rio Grande Valley School of
Medicine, , Edinburg, , TX,

, Department of Clinical Research, , Valley Baptist Medical Center, , Harlingen,
, TX,

, Neuroscience Department, , Valley Baptist Medical Center, , Harlingen, , TX,

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Originally published7 Sep 2022https://doi.org/10.1161/SVIN.121.000224Stroke:
Vascular and Interventional Neurology. 2022;2:e000224
* Other version(s) of this article

* YOU ARE VIEWING THE MOST RECENT VERSION OF THIS ARTICLE. PREVIOUS
VERSIONS:

* September 7, 2022: Ahead of Print

ABSTRACT

BACKGROUND

The Viz.ai artificial intelligence (AI) software module Viz large‐vessel
occlusion (LVO) utilizes AI‐powered LVO detection and triage technology to
automatically identify suspected LVOs through computed tomographic angiogram
imaging and alert on‐call stroke teams. We performed this analysis to determine
whether the Viz LVO software can reduce the door‐in to puncture time interval
within a comprehensive stroke center (CSC) for patients requiring endovascular
treatment.

METHODS

We conducted a retrospective chart review that compared the time interval
between patient arrival in the emergency department (door‐in) to puncture for
patients who presented consecutively with a stroke code to our CSC between
November 2016 and May 2020. The implementation of the AI software (Viz LVO) at
the CSC was in November 2018. Using a prospectively collected database at the
CSC, demographics, and outcomes were examined. This study was based on
evaluating real‐world practice.

RESULTS

We analyzed 86 patients from the pre‐AI study phase (average age, 68.53±13.13
years, 40.7% female) and 102 patients from the post‐AI study phase (average age,
69.87±15.75 years, 43.1% women). Following the implementation of the software,
the mean door‐in to puncture time interval within the CSC significantly improved
by 86.7 minutes (206.6 versus 119.9 minutes; P<0.001); significant improvements
were also noted in the rate of reperfusion (modified Thrombolysis in Cerebral
Infraction 2B‐3) for patients in the post‐AI population (P=0.036).

CONCLUSION

The incorporation of the software was associated with a significant improvement
in treatment time within the CSC, as well as significantly higher rates of
adequate reperfusion. Prospective, multicenter, controlled studies with a larger
cohort are warranted to expand on the ability of Viz LVO to improve treatment
times and outcomes for patients with an LVO stroke.

* Download figure

NONSTANDARD ABBREVIATIONS AND ACRONYMS

CSC

comprehensive stroke center

CTA

computed tomographic angiogram

HERMES

Highly Effective Reperfusion Using Multiple Endovascular Devices

IV tPA

intravenous tissue plasminogen activator

LVO

large‐vessel occlusion

mRS

modified Rankin scale

mechanical thrombectomy

NIHSS

National Institutes of Health Stroke Scale

TICI

Thrombolysis in Cerebral Infraction

CLINICAL PERSPECTIVE

* What is new?

* Viz large‐vessel occlusion (LVO) is a clinically validated artificial
intelligence technology, which was implemented in our hub‐and‐spoke network
in late 2018.

* Viz LVO is indicated to automatically detect suspected LVOs using artificial
intelligence in order to provide fast triage across hub‐and‐spoke networks or
within thrombectomy‐capable institutions.

* What are the clinical implications?

* The implementation of artificial intelligence software such as Viz LVO may be
an effective tool in minimizing treatment delivery time for patients with
stroke who have LVOs.

Mechanical thrombectomy (MT) is proven to be effective and safe in the treatment
of intracranial large‐vessel occlusion (LVO) and improves patients’ clinical
outcomes based on the results of 6 randomized clinical trials.1, 2, 3, 4, 5, 6
However, the length of time between symptom onset and MT remains critical to the
outcome of the patient, as it has been noted that delays in delivering MT can
significantly reduce rates of satisfactory outcomes.7 Underlying issues are
present in the current systems of care, many of those pertaining to transfer
between primary and comprehensive (thrombectomy capable) stroke centers as well
as the protracted process, which occurs after the patient has arrived at the
comprehensive stroke center (CSC).8, 9 The idea of minimizing treatment delivery
time remains paramount in providing the standard of care for patients with LVO.

Improved efficiency in the detection, triage, and care coordination along with
in‐hospital management has shown a strong ability to reduce the time from
symptom onset to the time of treatment for patients with an LVO.10 A newly
introduced artificial intelligence (AI) software known as Viz LVO is a software
device designed to analyze and detect an LVO on a computed tomographic angiogram
(CTA) and send a notification to the stroke care team at the medical facility
via the Viz LVO mobile app, where they can use the image viewer within the app
to review the result. The app has a HIPAA (Health Insurance Portability and
Accountability Act)–compliant messaging platform that facilitates communication
among the care team. Viz LVO software operates as a parallel workflow tool
independent of standard‐of‐care workflow. The medical facility selects who will
be part of the care team that gets access to the Viz mobile application. The
software module is installed across the stroke network in healthcare
institutions, including the CSC. It is used by hospital networks and trained
clinicians to identify and communicate images of specific patients to a
neurovascular specialist, such as a neurointerventional surgeon. The system
automatically receives CTA studies of patients with suspected stroke and
analyzes the CTAs for image features that may indicate the presence of an LVO.
The Viz LVO software analyzes a scan and when it detects a potential LVO, each
member of the on‐call stroke care team is automatically alerted via a
notification to their mobile device. Images that are previewed through the
mobile application are compressed and are for informational purposes only. They
are not intended for diagnostic use.

Recent Viz LVO studies have shown promising results in which treatment times,
defined as the time of CTA at the primary care center and door‐in at the CSC,
have been significantly reduced.11 Furthermore, recent preliminary studies have
shown a significant improvement in the 5‐day National Institutes of Health
Stroke Scale (NIHSS), discharge modified Rankin scale (mRS), and median 90‐day
mRS scores after the implementation of Viz LVO.12 The primary focus of our study
was to compare the interval between the time between door‐in to the time of
puncture at the CSC pre‐AI (November 2016 to November 2018) and post‐AI
(December 2018 to May 2020). Pre‐AI is the window of time before the
implementation of the clinical application tool and post‐AI is after the
implementation of Viz. This study was performed to explore how AI software
impacts workflow after the patient with LVO has entered the CSC.

METHODS

The data that support the findings of this study are available from the
corresponding author on reasonable request. A retrospective study was conducted
including patients who originally presented to the CSC, all with signs of LVO.
The study received local institutional review board approval of waiver of
consent and is in compliance with the Health Insurance Portability and
Accountability Act. The data that support the findings of this study—including
individual participant data after deidentification—are available from the
corresponding author on reasonable request. Data may be shared with
investigators whose proposed use of the data has been approved by an independent
review committee identified for this purpose and may be used for individual
participant data meta‐analysis. The STROBE (Strengthening the Reporting of
Observational Studies in Epidemiology) research and reporting guidelines were
utilized for the preparation of this article.

The AI software utilized in this study was Viz LVO (Viz.ai, Inc.). Viz LVO is a
parallel workflow tool cleared by the United States Food and Drug Administration
under the name Viz ContaCT to analyze CTA images of the brain acquired in the
acute setting, send notifications that a suspected LVO has been identified, and
put together a review of those images. The Viz LVO algorithm has the capacity to
detect LVOs involving the proximal intracranial anterior circulation. The
current algorithm has not yet been trained to detect internal carotid artery
occlusions proximal to the supraclinoid segment or posterior circulation
occlusions. Viz LVO was implemented as a commercial device at the CSC and this
study is a chart review of standard‐of‐care records. The risks and benefits of
endovascular therapy treatment were discussed with patients’ families. A
prospectively maintained database was compiled at the CSC where the process for
data collection was reviewed and approved at the institution. All patient
details regarding procedures were recorded and stored. Follow‐up imagery
including rates of hemorrhage and mass effect were scored based on information
prospectively collected by the physicians.

For both cohorts, the inclusion criteria included patients who presented
consecutively with LVO on CTA at the CSC, were treated with MT with or without
angioplasty and stenting in the catheterization laboratory, and had an magnetic
resonance imaging and noncontrast computed tomography follow‐up imaging 24±4
hours after intervention. An additional inclusion criterion for patients in the
post‐AI software population was the requirement of being flagged by Viz LVO as
potentially having an LVO. The exclusion criteria included patients who were
transferred from neighboring primary stroke centers and those who achieved
vessel reperfusion through means other than thrombectomy, including adequate
reperfusion from intravenous tissue plasminogen activator (IV tPA) alone or
natural clot dissolution. All patients who arrived at the CSC were divided into
the 2 groups based on whether they presented between November 2016 and November
2018 (pre‐AI software implementation) or between December 2018 and May 2020
(post‐AI software implementation). Patient triage before the implementation of
Viz LVO involved a linear stepwise mechanism from the time the patient arrived
at the CSC emergency department. First, the patient would be treated by the
emergency department physician and referred to CTA scan, which was conducted by
the technologist. The scan would then be read by the radiologist who refers this
information to the emergency department physician and neurologist. The
neurologist would then recommend care and refer this information to the
interventionalist who would perform the thrombectomy. Patient triage following
the implementation of Viz LVO involved a more unified schematic in which the AI
system sends triage alerts of the CTA scan to the mobile devices of the
emergency department physician, radiologist, neurologist, and interventionalist
simultaneously. The Viz LVO notification was the first notification to the
on‐call stroke team. The patient would then be treated by the interventionalist
based on the unified communication of all involved parties. These triage methods
are represented in Figure 1.

* Download figure
* Download PowerPoint

Figure 1. Depiction of the comprehensive stroke center workflow before and after
the implementation of the AI software using mean time of door‐in to puncture.

A.I. indicates artificial intelligence; CTA, computed tomographic angiogram; ED,
emergency department.

* Download figure
* Download PowerPoint

Figure 2. Trend lines plotting approximate 4‐month interval average
door‐to‐puncture times in minutes throughout the duration of the study from
November 3, 2016, to May 10, 2020.

AI indicates artificial intelligence.

DATA COLLECTED

Patients were selected consecutively from the chart based on the inclusion and
exclusion criteria listed in the Methods section. Baseline variables and
clinical, radiographic, and safety outcome rates were included. Baseline
variables included age, sex, ethnicity, admission NIHSS score, time from door‐in
to time of puncture (treatment time), time from CTA to puncture in the
catheterization laboratory (CTA‐to‐puncture), and patients’ medical history of
hypertension, atrial fibrillation, coronary artery disease, smoking status,
diabetes, and history of stroke/transient ischemic attack. Safety outcomes
included rates of hemorrhagic transformation, mass effect, symptomatic
intracerebral hemorrhage (ICH), and asymptomatic ICH. Symptomatic ICH was
classified in this study by the European Cooperative Acute Stroke Study's
definition as any type of ICH on any posttreatment imaging after the start of
thrombolysis and an increase of ≥4 NIHSS points from baseline, or from the
lowest value within 7 days, or leading to death. Magnetic resonance imaging and
noncontrast computed tomography were utilized for all follow‐up imaging, which
was done 24±4 hours after intervention. Measures of mass effect were evaluated
in cerebral edema, including changes in hemisphere volume, cerebral spinal fluid
volumetric analysis, and midline shift. Clinical outcomes included length of
stay (LOS) in the hospital and mortality rates. The radiographic outcome was the
rate of reperfusion (modified Thrombolysis in Cerebral Infraction [TICI] ≥2B),
which refers to postprocedural digital subtraction angiography.

STATISTICAL ANALYSIS

For this data set, we performed univariate analysis of the baseline variables
and outcomes, which included t test for continuous variables (eg, age and NIHSS
on admission), z test for comorbid conditions and outcomes, and chi‐square test
for categorical data to identify differences in baseline characteristics (sex,
race/ethnicity, and occlusion location). Outcomes in comparison included
post‐TICI 2B to 3, mortality rates at discharge, symptomatic hemorrhage rates,
and overall LOS. P values for the median associated with LOS were calculated
through the utilization of the Mann–Whitney U test in order to compare outcomes
between the independent groups. A significance level of 0.05 was chosen for this
statistical analysis. Mortality postdischarge from CSC was not tracked.

To adjust for imbalances between patients treated before and after the
introduction of the AI software, logistic regression analyses were performed to
determine the correlation between the AI software and: (1) good TICI score (2B
to 3), (2) symptomatic ICH, and (3) mortality rate. In the model analysis, the
categorical variables included were age, IV tPA use, and coronary artery
disease. This analysis was performed using MedCalc statistical software (MedCalc
Software Ltd).

RESULTS

There was a total of 188 patients during the study period (mean age,
69.26±14.55 years; 42.0% women) who initially presented to the CSC. Analysis of
86 patients from the pre‐AI software group (mean age, 68.53±13.13 years; 40.7%
women) and 102 patients from the post‐AI software group (mean age,
69.87±15.75 years; 43.1% women) was performed. The mean NIHSS on admission for
patients before AI software was 16.13±8.33 compared with 15.91±7.10 for patients
after AI software (P=0.847). IV tPA use at the CSC was similar between the 2
groups, with 36.0% (31 of 86) of receiving it in the pre‐AI software group and
34.3% (35 of 102) of patients receiving it in the post‐AI software group
(P=0.804). The time interval from time of patient last known well was included
for both populations with an average of 115.4±123.8 minutes for the pre‐AI
population and an average of 126.8±137.6 minutes (P=0.549); the median value for
this metric is also included in Table 1. All baseline and procedural data were
available for all patients treated with MT, and all patient data were included
in the analysis. Results of the univariate analysis for baseline demographics
and clinical characteristics are summarized in Table 1.

John Wiley & Sons, Ltd.

TABLE 1. BASELINE DEMOGRAPHICS AND CLINICAL CHARACTERISTICS OF PATIENTS WITH
ISCHEMIC STROKE WHO UNDERWENT THROMBECTOMY BEFORE AND AFTER THE IMPLEMENTATION
OF AI SOFTWARE

CharacteristicsOutcomesP valuePre‐AI software (n=86)Post‐AI software (n=102)Age
(mean±SD)68.53±13.1369.87±15.750.525Sex0.736Men51 (59.3%)58 (56.9%)Women35
(40.7%)44 (43.1%)Race or ethnicity0.391White16 (18.6%)26 (25.5%)Hispanic68
(79.1%)78 (76.5%)Black1 (1.2%)0 (0.0%)Asian1 (1.2%)0 (0.0%)NIHSS on
admission16.13±8.3315.91±7.100.847IV tPA use31 (36.0%)35 (34.3%)0.804Comorbid
conditionsDiabetes mellitus45 (52.3%)51 (50.0%)0.751Hypertension69 (80.2%)81
(79.4%)0.889Atrial fibrillation19 (22.1%)21 (20.6%)0.801History of stroke/TIA23
(26.7%)24 (23.5%)0.612Coronary artery disease17 (19.8%)31 (30.4%)0.096Cigarette
smoking7 (8.1%)9 (8.8%)0.867Time interval, minLast known well to time of
arrival, mean±SD115.4±123.8126.8±137.60.549Last known well to time of arrival,
median [IQR]55 [41–114]66 [39–151]0.603Occlusion location0.783Right MCA M1
segment2525Right MCA M2 segment612Left MCA M1 segment2325Left MCA M2
segment1314Right ICA‐terminus1112Left ICA‐terminus814

Significance level: P=0.05. AI indicates artificial intelligence; ICA, internal
carotid artery; IQR, interquartile range; IV tPA, intravenous tissue plasminogen
activator; MCA, middle cerebral artery; NIHSS, National Institutes of Health
Stroke Scale; SD, standard deviation; and TIA, transischemic attack.

The mortality rate at discharge did not show a statistical difference between
the 2 groups (P=0.789). Reperfusion was successful (modified TICI 2B to 3) in
84.9% (73 of 86) of patients in the pre‐AI software group compared with 94.1%
(96 of 102) of patients in the post‐AI software group, which yielded statistical
significance (P=0.036). In the pre‐AI population, 71 patients received 1 or 2
passes of the thrombectomy device, 12 patients received 3 or 4 passes of the
thrombectomy device, and 3 patients received 5 or 6 passes of the thrombectomy
device. In the post‐AI population, 76 patients received 1 or 2 passes of the
device, 22 received 3 or 4 passes of the device, and 4 received 5 or 6 passes of
the device. The average value for the number of passes did not show a
significant difference between the 2 populations (P=0.704). LOS was similar
between the 2 groups, with a median LOS of 7 [4–11] for the pre‐AI group and a
median LOS of 7.5 [4–12] for the post‐AI group (P=0.103). An average door‐in to
puncture time of 119.9 minutes was noted in the post‐AI software group, whereas
in the pre‐AI software group, an average door‐in to puncture time of
206.6 minutes was noted (P<0.001). An average CTA to patient time of arrival in
the procedure room of 54.3 minutes was noted in the post‐AI software group,
whereas in the pre‐AI software group, an average CTA‐to‐procedure room time of
91.1 minutes was noted (P<0.001). An average CTA‐to‐puncture time of
78.6 minutes was noted in the post‐AI software group, whereas in the pre‐AI
software group, an average CTA‐to‐puncture time of 117.2 minutes was noted
(P<0.001). Results of univariate analysis for treatment outcomes are summarized
in Table 2. The median and interquartile ranges for these time metrics are also
included in Table 2.

John Wiley & Sons, Ltd.

TABLE 2. OUTCOMES OF PATIENTS WITH ISCHEMIC STROKE WHO UNDERWENT THROMBECTOMY
BEFORE AND AFTER THE IMPLEMENTATION OF AI SOFTWARE

CharacteristicsOutcomesP valuePre‐AI software (n=86)Post‐AI software (n=102)Time
intervals, minDoor‐in to puncture, mean±SD206.6±169.1119.9±83.0<0.001Door‐in to
puncture, median [IQR]143 [114–234.5]94 [83–129]<0.001CTA‐to‐procedure room,
mean±SD91.1±73.354.3±38.8<0.001CTA‐to‐procedure room, median [IQR]72 [44–113]44
[33–64]<0.001CTA‐to‐puncture, mean±SD117.2±81.378.6±44.5<0.001CTA‐to‐puncture,
median [IQR]96 [61–128]69 [56–90]<0.001Thrombolysis in Cerebral InfarctionGood
(post‐TICI 2B to 3)73 (84.9%)96 (94.1%)0.036Poor (post‐TICI 0 to 2A)13 (15.1%)6
(5.9%)0.036Thrombectomy passes utilized,
mean±SD1.71±1.021.77±1.140.704In‐hospital complicationSymptomatic intracerebral
hemorrhage7 (8.1%)6 (5.9%)0.543Asymptomatic intracerebral hemorrhage2 (2.3%)5
(4.9%)0.353Hemorrhagic transformation6 (7.0%)13 (12.7%)0.191Mass effect12
(14.0%)5 (4.9%)0.031Length of stay, median [IQR]Admission to discharge7
[4–11]7.5 [4–12]0.103Mortality at discharge18 (20.9%)23 (22.5%)0.789

Significance level: P=0.05. AI indicates artificial intelligence; IQR,
interquartile range; and TICI, modified Thrombolysis in Cerebral Infarction.

The results of the time trend analysis are displayed in Figure 2. There was a
significant improvement in the time trend analysis between the time intervals of
March to November 2018 and December to July 2019 (P=0.011). These periods only
encompass a fraction of the entirety of the pre‐AI and post‐AI time intervals,
which were included in the study. Additionally, from the beginning of the pre‐AI
software in November 2016 to November 2018, although there was a slight downward
trend, there was no significant improvement (P=0.399).

Variables including age, IV tPA use, and rates of coronary artery disease were
analyzed in the multivariate analysis. IV tPA was given before CTA. The results
from the logistic regression analyses are summarized in Table 3. The odds of
good clinical outcome (odds ratio [OR], 1.67; [95% CI, 0.672–3.272]), good
modified TICI score (OR, 2.98; [95% CI, 1.153–3.282]), rate of symptomatic ICH
(OR, 2.24; [95% CI, 0.868–2.483]), and mortality (OR, 1.11; [95% CI,
0.454–1.467]) were similar among those who underwent treatment following AI
implementation after adjusting for potential confounding variables.

John Wiley & Sons, Ltd.

TABLE 3. MULTIVARIATE ANALYSIS EVALUATING EFFECT OF THE AI SOFTWARE ON OUTCOMES
OF PATIENTS WITH LVO

OutcomesUnadjustedAdjusted for age, IV tPA use, and coronary artery diseaseOR
(95% CI)P valueOR (95% CI)P valueGood TICI score (2B to 3)2.85
(1.034–7.855)0.0362.98 (1.153–3.282)0.034Symptomatic hemorrhage0.71
(0.228–2.184)0.5432.24 (0.868–2.483)0.597Mortality rate1.10
(0.548–2.208)0.7891.11 (0.454–1.467)0.265

AI indicates artificial intelligence; IV tPA, intravenous tissue plasminogen
activator; LVO, large‐vessel occlusion; TICI, modified Thrombolysis in Cerebral
Infarction; and OR, odds ratio.

DISCUSSION

The study represents a retrospective series of patients with LVO who presented
to a CSC before and after the implementation of AI software. Our study has shown
that the implementation of AI software has improved workflow within the CSC, as
it was seen that time from door‐in to time of puncture was significantly reduced
by an average of 86.7 minutes (P<0.001), as well as the reduced time from time
of CTA‐to‐puncture (P<0.001). Along with this, significantly higher rates of
adequate reperfusion (modified TICI 2B to 3) were noted in the population
treated after the implementation of the AI software (P=0.036).

A recent study conducted in 2544 patients among 139 hospitals in the United
States demonstrated a 96.3% [92.7%–98.8%] sensitivity rate and a 93.8%
[92.8%–94.8%] specificity rate in identifying LVOs using scanners from an array
of manufacturers.13, 14 A time trend analysis of the data in the aforementioned
study was performed in order to understand whether the incorporation of AI
software can expedite workflow after the patient with LVO has entered the CSC. A
similar form of analysis was performed in this study to address limitations
regarding the improvement of workflow over time as endovascular treatment
volumes and experience increase. In particular, in a recent study by Rodrigues
et al, Viz‐LVO software was utilized to analyze 610 CTAs, identifying suspected
LVOs of the ICA‐T and the MCA with a sensitivity of 87.6%, specificity of 88.5%,
and an accuracy of 87.9%, rejecting 2.5% of the CTAs because of poor image
quality.15 Of these CTAs taken, the mean run time of the algorithm was
2.78± 0.5 minutes.15 An additional study by Chatterjee et al analyzed 650 CTAs
in which Viz LVO demonstrated a sensitivity of 82%, specificity of 94%, positive
predictive value of 77%, and negative predictive value of 95%. Forty‐one cases
in this study were not processed because of metal artifact, inadequate contrast,
motion artifact, or excess z‐spacing variability. The mean processing time for
the Viz LVO software was 5 minutes, with a maximum of 8 minutes for all of the
studies.16

Our study emphasizes the importance of reducing treatment time interval within
the CSC by expediting workflow and improving patient care. This metric is
important when analyzed through the lens of “time is brain” (with the typical
patient losing 1.9 million neurons each minute in which the LVO is left
untreated).17 The American Heart Association/American Stroke Association's
updated guidelines and Target: Stroke Phase III goal strives for door‐in to
puncture times (arrival to first pass with thrombectomy device) with 90 minutes
for direct‐arriving patients and within 60 minutes for transfer patients in ≥50%
of acute ischemic stroke cases treated with endovascular therapy.18 It is true
that the door‐in to puncture achieved in our study was higher than this
recommended guideline. However, we perceive these data to express a relevant
improvement specifically within this CSC.

Previous studies have shown the association between treatment time and primary
outcomes in patients diagnosed with acute ischemic stroke. Jahan et al
retrospectively analyzed >6700 patients diagnosed with an acute ischemic stroke,
finding that for every 15 minutes saved during treatment, rates of independent
ambulation had an absolute increase of 1.14%, functional independence at
discharge had an absolute increase of 0.91%, and lower mortality at discharge
had an absolute increase of −0.77%.19 Furthermore, Man et al in retrospective
analysis of over 60 000 patients reported each 15‐minute increase in door‐in to
puncture time to be significantly associated with higher rates of mortality
(adjusted hazard ratio, 1.04; 95% CI, 1.02–1.05) and significantly higher rates
of hospital readmission (adjusted hazard ratio, 1.02; 95% CI, 1.01–1.03).20 In
our study, the door‐in to puncture and CTA‐to‐puncture time intervals were
significantly improved in the post‐AI population (P<0.001).

Along with improved door‐in to puncture and CTA‐to‐puncture times, the post‐AI
treatment group experienced significantly higher rates of adequate reperfusion
(P=0.036) and significantly lower rates of postoperative complications such as
mass effect (P=0.031). The association of faster treatment times with higher
rates of adequate reperfusion is consistent with prior surgical thrombectomy and
thrombolytic drug studies.21, 22, 23 In the HERMES (Highly Effective Reperfusion
Using Multiple Endovascular Devices) meta‐analysis of 728 patients who underwent
thrombectomy, the rate of successful reperfusion (TICI 2B to 3) decreased with
increasing time from door‐in to puncture.23 Two possible mechanisms for
reperfusion challenges with increasing time were proposed: (1) an unfavorable
modification of thrombus composition and properties, and (2) the presence of
more neutrophil extracellular traps (fibrous networks) that increase thrombus
adherence to the arterial wall and make the thrombus more resistant to
mechanical destruction.23 In the present study, the higher rate of successful
reperfusion after implementation of the AI software is consistent with the
decrease in door‐in to puncture times as observed in the HERMES study, but a
larger data set is needed to discover the magnitude of the association with
adjustments for potentially confounding variables such as thrombectomy device
(not recorded), number of passes, and prior administration of tPA. The number of
thrombectomy passes and IV tPA use were not significantly different between the
pre‐AI and post‐AI software groups.

An important aspect that must be addressed to further validate the results of
our study is the utilization of IV tPA at the CSC before MT. There was a similar
rate of IV tPA administered to patients in the 2 study populations (36.0% pre‐AI
versus 34.3% post‐AI, P=0.804). The administration of IV tPA has been known to
lengthen the door‐in to puncture time for patients presenting with signs of
LVO.24, 25 The similar rates of IV tPA among the 2 study populations have
allowed for an analysis focusing primarily on the impact of AI software.

Our study suggests that the utilization of AI stroke software in the stroke
network has the potential to improve outcomes among patients with LVO. A
significant reduction in door‐in to puncture times, improvement in reperfusion
rates, and a significantly lower rate of mass effect were noted after the
incorporation of the AI software. Time spent within the CSC before MT may
represent the single biggest factor that can improve reperfusion rates for
patients. These findings represent the idea that AI software may serve as a
powerful tool in stroke care and may lead to vital opportunities in improving
treatment times and patient outcomes within stroke networks.

LIMITATIONS

The main limitations in this study are its retrospective nature and relatively
small sample sizes. The small sample sizes may decrease the power to detect
differences among subgroups of patients for end points such as mortality and
good outcomes. However, our current sample size was poised only to identify
large differences in end points, and therefore is primarily hypothesis
generating in our study design. The interpretation of mRS clinical outcomes is
limited by not having available baseline mRS scores. Nevertheless, a significant
difference between the pre‐AI and post‐AI cohorts was not observed in discharge
or 90‐day mRS scores. Future studies that assess mRS outcomes will need to be
powered appropriately and use data sets that include baseline mRS. Additionally,
it is important to recognize that AI software can sometimes have false‐positive
and false‐negative alerts, and variations in door‐in to puncture times may vary
slightly throughout the years as a result of changes in standards of care,
various treatment guidelines, and organizational changes such as the
notification of the neurologist of suspected LVO by the emergency room doctor
before imaging. Larger multicenter prospective studies are necessary to
corroborate the results of our study.

CONCLUSIONS

The implementation of the Viz LVO module within this stroke network reduced the
time interval from when the patient arrived at the center to the moment of
puncture, which, in turn, may have improved revascularization rates and reduced
the risk of postoperative complications through this reduced treatment time.
These data further the idea that AI software along with an improved standard of
care workflow is an effective tool that may allow for improved patient outcomes.
More extensive studies are warranted to expand on the ability of AI technology
such as Viz LVO to improve treatment times and outcomes in patients with LVO.

SOURCES OF FUNDING

The authors have not declared a specific grant for this research from any
funding agency in the public, commercial, or not‐for‐profit sectors.

DISCLOSURES

Ameer E. Hassan is a consultant for Medtronic, Microvention, Penumbra, Stryker,
Genentech, Balt, Viz.ai, and GE Healthcare.

ACKNOWLEDGMENTS

Ameer E. Hassan provided research questions, analyzed the data, and revised the
article. Victor M. Ringheanu developed the statistical analyses and drafted and
revised the article. Laurie Preston revised the paper. Wondwossen G. Tekle
revised the paper.

FOOTNOTES

*Correspondence to: Ameer E. Hassan, Department of Neurology, University of
Texas Rio Grande Valley School of Medicine, Edinburg, TX 78539. E‐mail:
ameerehassan@gmail.com

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Keywords
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