The True Potential of Artificial Intelligence for Detection of Large-Vessel Occlusion: The Role of M2 Occlusions

Background and Purpose: Randomized controlled trials have demonstrated the importance of time to endovascular therapy (EVT) in clinical outcomes in large vessel occlusion (LVO) acute ischemic stroke. Delays to treatment are particularly prevalent when patients require a transfer from hospitals without EVT capability onsite. A computer-aided triage system, Viz LVO, has the potential to streamline workflows. This platform includes an image viewer, a communication system, and an artificial intelligence (AI) algorithm that automatically identifies suspected LVO strokes on CTA imaging and rapidly triggers alerts. We hypothesize that the Viz application will decrease time-to-treatment, leading to improved clinical outcomes. Methods: A retrospective analysis of a prospectively maintained database was assessed for patients who presented to a stroke center currently utilizing Viz LVO and underwent EVT following transfer for LVO stroke between July 2018 and March 2020. Time intervals and clinical outcomes were compared for 55 patients divided into pre- and post-Viz cohorts. Results: The median initial door-to-neuroendovascular team (NT) notification time interval was significantly faster (25.0 min [IQR = 12.0] vs. 40.0 min [IQR = 61.0]; p = 0.01) with less variation (p < 0.05) following Viz LVO implementation. The median initial door-to-skin puncture time interval was 25 min shorter in the post-Viz cohort, although this was not statistically significant (p = 0.15). Conclusions: Preliminary results have shown that Viz LVO implementation is associated with earlier, more consistent NT notification times. This application can serve as an early warning system and a failsafe to ensure that no LVO is left behind.

Download Full-text

Role of Non-Perfusion Factors in Mildly Symptomatic Large Vessel Occlusion Stroke

Journal of Stroke and Cerebrovascular Diseases ◽

10.1016/j.jstrokecerebrovasdis.2020.105172 ◽

2020 ◽

Vol 29 (10) ◽

pp. 105172

Author(s):

Omar Hussein ◽

Ahmed Abd Elazim ◽

Khalid Sawalha ◽

Smeer Salam ◽

Kasser Saba ◽

...

Keyword(s):

Large Vessel ◽

Large Vessel Occlusion ◽

Vessel Occlusion

Download Full-text

Artificial Intelligence for Large Vessel Occlusion Stroke: A Systematic Review

World Neurosurgery ◽

10.1016/j.wneu.2021.12.004 ◽

2021 ◽

Author(s):

Nathan A. Shlobin ◽

Ammad A. Baig ◽

Muhammad Waqas ◽

Tatsat R. Patel ◽

Rimal H. Dossani ◽

...

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Large Vessel ◽

Large Vessel Occlusion ◽

Vessel Occlusion

Download Full-text

Epidemiological Surveillance of the Impact of the COVID-19 Pandemic on Stroke Care Using Artificial Intelligence

Stroke ◽

10.1161/strokeaha.120.031960 ◽

2021 ◽

Author(s):

Raul G. Nogueira ◽

Jason M. Davies ◽

Rishi Gupta ◽

Ameer E. Hassan ◽

Thomas Devlin ◽

...

Keyword(s):

Artificial Intelligence ◽

Acute Stroke ◽

Systems Of Care ◽

Ct Perfusion ◽

Stroke Care ◽

Large Vessel ◽

Large Vessel Occlusion ◽

Vessel Occlusion ◽

Number Of Patients ◽

The Impact

Background and Purpose: The degree to which the coronavirus disease 2019 (COVID-19) pandemic has affected systems of care, in particular, those for time-sensitive conditions such as stroke, remains poorly quantified. We sought to evaluate the impact of COVID-19 in the overall screening for acute stroke utilizing a commercial clinical artificial intelligence platform. Methods: Data were derived from the Viz Platform, an artificial intelligence application designed to optimize the workflow of patients with acute stroke. Neuroimaging data on suspected patients with stroke across 97 hospitals in 20 US states were collected in real time and retrospectively analyzed with the number of patients undergoing imaging screening serving as a surrogate for the amount of stroke care. The main outcome measures were the number of computed tomography (CT) angiography, CT perfusion, large vessel occlusions (defined according to the automated software detection), and severe strokes on CT perfusion (defined as those with hypoperfusion volumes >70 mL) normalized as number of patients per day per hospital. Data from the prepandemic (November 4, 2019 to February 29, 2020) and pandemic (March 1 to May 10, 2020) periods were compared at national and state levels. Correlations were made between the inter-period changes in imaging screening, stroke hospitalizations, and thrombectomy procedures using state-specific sampling. Results: A total of 23 223 patients were included. The incidence of large vessel occlusion on CT angiography and severe strokes on CT perfusion were 11.2% (n=2602) and 14.7% (n=1229/8328), respectively. There were significant declines in the overall number of CT angiographies (−22.8%; 1.39–1.07 patients/day per hospital, P <0.001) and CT perfusion (−26.1%; 0.50–0.37 patients/day per hospital, P <0.001) as well as in the incidence of large vessel occlusion (−17.1%; 0.15–0.13 patients/day per hospital, P <0.001) and severe strokes on CT perfusion (−16.7%; 0.12–0.10 patients/day per hospital, P <0.005). The sampled cohort showed similar declines in the rates of large vessel occlusions versus thrombectomy (18.8% versus 19.5%, P =0.9) and comprehensive stroke center hospitalizations (18.8% versus 11.0%, P =0.4). Conclusions: A significant decline in stroke imaging screening has occurred during the COVID-19 pandemic. This analysis underscores the broader application of artificial intelligence neuroimaging platforms for the real-time monitoring of stroke systems of care.

Download Full-text

Abstract WMP16: Artificial Intelligence Detection of Cerebrovascular Large Vessel Occlusion - Nine Month, 650 Patient Evaluation of the Diagnostic Accuracy and Performance of the Viz.ai LVO Algorithm

Stroke ◽

10.1161/str.50.suppl_1.wmp16 ◽

2019 ◽

Vol 50 (Suppl_1) ◽

Cited By ~ 5

Author(s):

Arindam Chatterjee ◽

Nayana R Somayaji ◽

Ismail M Kabakis

Keyword(s):

Artificial Intelligence ◽

Diagnostic Accuracy ◽

Large Vessel ◽

Patient Evaluation ◽

Large Vessel Occlusion ◽

Vessel Occlusion ◽

And Performance

Download Full-text

Role of Radiological Eye Deviation in Imaging Occult Cases of Acute Large Vessel Occlusion

Journal of Stroke and Cerebrovascular Diseases ◽

10.1016/j.jstrokecerebrovasdis.2020.104687 ◽

2020 ◽

Vol 29 (5) ◽

pp. 104687

Author(s):

Nan N. Jiang ◽

Wei Wu ◽

Crystal Fong ◽

Demetrios J. Sahlas ◽

Ramiro Larrazabal

Keyword(s):

Large Vessel ◽

Large Vessel Occlusion ◽

Vessel Occlusion

Download Full-text

Automated emergent large vessel occlusion detection by artificial intelligence improves stroke workflow in a hub and spoke stroke system of care

Journal of NeuroInterventional Surgery ◽

10.1136/neurintsurg-2021-017714 ◽

2021 ◽

pp. neurintsurg-2021-017714

Author(s):

Lucas Elijovich ◽

David Dornbos III ◽

Christopher Nickele ◽

Andrei Alexandrov ◽

Violiza Inoa-Acosta ◽

...

Keyword(s):

Artificial Intelligence ◽

Usual Care ◽

Linear Regression Analysis ◽

System Of Care ◽

Large Vessel ◽

Arterial Puncture ◽

Large Vessel Occlusion ◽

Vessel Occlusion ◽

Hub And Spoke ◽

Time Savings

BackgroundEmergent large vessel occlusion (ELVO) acute ischemic stroke is a time-sensitive disease.ObjectiveTo describe our experience with artificial intelligence (AI) for automated ELVO detection and its impact on stroke workflow.MethodsWe conducted a retrospective chart review of code stroke cases in which VizAI was used for automated ELVO detection. Patients with ELVO identified by VizAI were compared with patients with ELVO identified by usual care. Details of treatment, CT angiography (CTA) interpretation by blinded neuroradiologists, and stroke workflow metrics were collected. Univariate statistical comparisons and linear regression analysis were performed to quantify time savings for stroke metrics.ResultsSix hundred and eighty consecutive code strokes were evaluated by AI; 104 patients were diagnosed with ELVO during the study period. Forty-five patients with ELVO were identified by AI and 59 by usual care. Sixty-nine mechanical thrombectomies were performed.Median time from CTA to team notification was shorter for AI ELVOs (7 vs 26 min; p<0.001). Door to arterial puncture was faster for transfer patients with ELVO detected by AI versus usual care transfer patients (141 vs 185 min; p=0.027). AI yielded a time savings of 22 min for team notification and a 23 min reduction in door to arterial puncture for transfer patients.ConclusionsAI automated alerts can be incorporated into a comprehensive stroke center hub and spoke system of care. The use of AI to detect ELVO improves clinically meaningful stroke workflow metrics, resulting in faster treatment times for mechanical thrombectomy.

Download Full-text

Abstract WP61: Artificial Intelligence for Automated Detection of Large Vessel Occlusion Strokes

Stroke ◽

10.1161/str.51.suppl_1.wp61 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Catarina Perry da Câmara ◽

Gabriel M Rodrigues ◽

Clara Barreira ◽

Mehdi Bouslama ◽

Leonardo Pisani ◽

...

Keyword(s):

Artificial Intelligence ◽

Detrimental Effect ◽

Poor Quality ◽

Automated Detection ◽

Large Vessel ◽

Large Vessel Occlusion ◽

Accurate Identification ◽

Vessel Occlusion ◽

The Mean ◽

Stroke Center

Introduction: Identification of Large Vessel Occlusion (LVO) in acute ischemic stroke (AIS) patients is critical for proper decision-making. Limited availability of trained experts and delays in LVO recognition can have a detrimental effect on outcomes. We sought to evaluate an artificial intelligence-based algorithm for LVO detection in AIS. Methods: A retrospective analysis of a prospectively-collected database of AIS patients admitted to a large volume stroke center between 2014-2018 was performed. Experienced vascular neurologists graded CTA for presence and site of LVO. Concurrently, studies were analyzed by the Viz-LVO Algorithm® version 1.4 (GA) - a convolutional neural network programmed to detect occlusions from the internal carotid artery terminus (ICA-T) to the sylvian fissure, which would include all MCA M1-segment and most M2-segment lesions. CTA readings were categorized as LVOs (ICA-T, MCA-M1, MCA-M2) versus non-LVOs/more distal occlusions. Comparisons between human and AI-based readings were done by accuracy analysis and calculating Cohen’s kappa. Results: A total of 610 CTAs were analyzed. The AI algorithm rejected 3.4% of the CTAs due to poor quality. Viz-LVO identified LVOs with an overall sensitivity of 81.3%, specificity of 87.8%, and accuracy of 83.2% (AUC 0.845 (95%CI:0.81-0.88, p<0.001). Table 1 shows the results per occlusion site. Accuracy was higher for ICA-T and M1 occlusions as compared to M2 occlusions. The mean run time of the algorithm was 2.78±0.5minutes. Conclusion: Our study demonstrates that automated AI reading allows for fast and accurate identification of LVO strokes. Future efforts should be made to improve the detection of the more distal occlusions.

Download Full-text

Role of Favorable Perfusion Imaging in Predicting the Outcome of Patients with Acute Ischemic Stroke due to Large Vessel Occlusion Undergoing Effective Thrombectomy: A Single-Center Study

Cerebrovascular Diseases Extra ◽

10.1159/000513025 ◽

2021 ◽

pp. 1-8

Author(s):

Riccardo Di Iorio ◽

Fabio Pilato ◽

Iacopo Valente ◽

Andrea Laurienzo ◽

Simona Gaudino ◽

...

Keyword(s):

Computed Tomography ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Large Vessel ◽

Controlled Study ◽

Ischemic Penumbra ◽

Large Vessel Occlusion ◽

Vessel Occlusion ◽

Single Center Study

Introduction: We sought to verify the predicting role of a favorable profile on computed tomography perfusion (CTP) in the outcome of patients with acute ischemic stroke (AIS) due to large vessel occlusion (LVO) undergoing effective mechanical thrombectomy (MT). Methods: We retrospectively enrolled 25 patients with AIS due to LVO and with a CTP study showing the presence of ischemic penumbra who underwent effective MT, regardless of the time of onset. The controls were 25 AIS patients with overlapping demographics and clinical and computed tomography angiography features at admission who had undergone successful MT within 6 h from onset and without a previous CTP study. The outcome measure was the modified Rankin Scale (mRS) score at 90 days. Results: Sixty-four percent of the study patients had an mRS score of 0–1 at 90 days versus 12% of the control patients (p < 0.001). Patients of the study group had a more favorable distribution of disability scores (median mRS [IQR] score of 0 [0–2] vs. 2 [2–3]). Multivariate analysis showed that the selection of patients based on a favorable CTP study was strongly associated (p < 0.001) with a better neurological outcome. Conclusions: In our small-sized and retrospective study, the presence of ischemic penumbra was associated with a better clinical outcome in patients with AIS due to LVO after MT. In the future, a larger and controlled study with similar criteria of enrollment is needed to further validate the role of CTP in patient selection for MT, regardless of the time from the onset of symptoms.

Download Full-text