Segmentation of Acute Stroke Infarct Core Using Image-Level Labels on CT-Angiography

Acute ischemic stroke is a leading cause of death and disability in the world. Treatment decisions, especially around emergent revascularization procedures, rely heavily on size and location of the infarct core. Currently, accurate assessment of this measure is challenging. While MRI-DWI is considered the gold standard, its availability is limited for most patients suffering from stroke. Another well-studied imaging modality is CT-Perfusion (CTP) which is much more common than MRI-DWI in acute stroke care, but not as precise as MRI-DWI, and it is still unavailable in many stroke hospitals. A method to determine infarct core using CT-Angiography (CTA), a much more available imaging modality albeit with significantly less contrast in stroke core area than CTP or MRI-DWI, would enable significantly better treatment decisions for stroke patients throughout the world. Existing deep-learning-based approaches for stroke core estimation have to face the trade-off between voxel-level segmentation / image-level labels and the difficulty of obtaining large enough samples of high-quality DWI images. The former occurs when algorithms can either output voxel-level labeling which is more informative but requires a significant effort by annotators, or image-level labels that allow for much simpler labeling of the images but results in less informative and interpretable output; the latter is a common issue that forces training either on small training sets using DWI as the target or larger, but noisier, dataset using CT-Perfusion (CTP) as the target. In this work, we present a deep learning approach including a new weighted gradient-based approach to obtain stroke core segmentation with image-level labeling, specifically the size of the acute stroke core volume. Additionally, this strategy allows us to train using labels derived from CTP estimations. We find that the proposed approach outperforms segmentation approaches trained on voxel-level data and the CTP estimation themselves

Underutilization of Endovascular Therapy in Black Patients With Ischemic Stroke: An Analysis of State and Nationwide Cohorts

BACKGROUND AND PURPOSE: Endovascular therapy (EVT) is a very effective treatment but relies on specialized capabilities that are not available in every hospital where acute ischemic stroke is treated. Here, we assess whether access to and utilization of this therapy has extended uniformly across racial and ethnic groups. METHODS: We conducted a retrospective, population-based study using the 2019 Texas Inpatient Public Use Data File. Acute ischemic stroke cases and EVT use were identified using the RESULTS: Among 40 814 acute ischemic stroke cases in Texas in 2019, 54% were White, 17% Black, and 21% Hispanic. Black patients had similar admissions to EVT-performing hospitals and greater admissions to comprehensive stroke centers (CSCs) compared with White patients (EVT 62% versus 62%, CONCLUSIONS: We found no evidence of disparity in presentation to EVT-performing hospitals or CSCs; however, lower rates of EVT were observed in Black patients

Synthetic Oct-a Blood Vessel Maps Using Fundus Images and Generative Adversarial Networks

Vessel segmentation in fundus images permits understanding retinal diseases and computing image-based biomarkers. However, manual vessel segmentation is a time-consuming process. Optical coherence tomography angiography (OCT-A) allows direct, non-invasive estimation of retinal vessels. Unfortunately, compared to fundus images, OCT-A cameras are more expensive, less portable, and have a reduced field of view. We present an automated strategy relying on generative adversarial networks to create vascular maps from fundus images without training using manual vessel segmentation maps. Further post-processing used for standard en face OCT-A allows obtaining a vessel segmentation map. We compare our approach to state-of-the-art vessel segmentation algorithms trained on manual vessel segmentation maps and vessel segmentations derived from OCT-A. We evaluate them from an automatic vascular segmentation perspective and as vessel density estimators, i.e., the most common imaging biomarker for OCT-A used in studies. Using OCT-A as a training target over manual vessel delineations yields improved vascular maps for the optic disc area and compares to the best-performing vessel segmentation algorithm in the macular region. This technique could reduce the cost and effort incurred when training vessel segmentation algorithms. To incentivize research in this field, we will make the dataset publicly available to the scientific community

Women With Large Vessel Occlusion Acute Ischemic Stroke Are Less Likely to Be Routed to Comprehensive Stroke Centers

Background Prehospital routing of patients with large vessel occlusion (LVO) acute ischemic stroke (AIS) to centers capable of performing endovascular therapy may improve clinical outcomes. Here, we explore whether distance to comprehensive stroke centers (CSCs), stroke severity, and sex are associated with direct‐to‐CSC prehospital routing in patients with LVO AIS. Methods and Results In this cross‐sectional study, we identified consecutive patients with LVO AIS from a prospectively collected multihospital registry throughout the greater Houston area from January 2019 to June 2020. Primary outcome was prehospital routing to CSC and was compared between men and women using modified Poisson regression including age, sex, race or ethnicity, first in‐hospital National Institutes of Health Stroke Scale score, travel time, and distances to the closest primary stroke center and CSC. Among 503 patients with LVO AIS, 413 (82%) were routed to CSCs, and women comprised 46% of the study participants. Women with LVO AIS compared with men were older (73 versus 65, P\u3c0.01) and presented with greater National Institutes of Health Stroke Scale score (14 versus 12, P=0.01). In modified Poisson regression, women were 9% less likely to be routed to CSCs compared with men (adjusted relative risk [aRR], 0.91 [0.84–0.99], P=0.024) and distance to nearest CSC ≤10 miles was associated with 38% increased chance of routing to CSC (aRR, 1.38 [1.26–1.52], P\u3c0.001). Conclusions Despite presenting with more significant stroke syndromes and living within comparable distance to CSCs, women with LVO AIS were less likely to be routed to CSCs compared with men. Further study of the mechanisms behind this disparity is needed

Women With Large Vessel Occlusion acute Ischemic Stroke are Less Likely to Be Routed to Comprehensive Stroke Centers

Background Prehospital routing of patients with large vessel occlusion (LVO) acute ischemic stroke (AIS) to centers capable of performing endovascular therapy may improve clinical outcomes. Here, we explore whether distance to comprehensive stroke centers (CSCs), stroke severity, and sex are associated with direct-to-CSC prehospital routing in patients with LVO AIS. Methods and Results In this cross-sectional study, we identified consecutive patients with LVO AIS from a prospectively collected multihospital registry throughout the greater Houston area from January 2019 to June 2020. Primary outcome was prehospital routing to CSC and was compared between men and women using modified Poisson regression including age, sex, race or ethnicity, first in-hospital National Institutes of Health Stroke Scale score, travel time, and distances to the closest primary stroke center and CSC. Among 503 patients with LVO AIS, 413 (82%) were routed to CSCs, and women comprised 46% of the study participants. Women with LVO AIS compared with men were older (73 versus 65

The Society of Vascular and Interventional Neurology (SVIN) Mechanical Thrombectomy Registry: Methods and Primary Results

Background A better understanding of real‐world practice patterns in the endovascular treatment for large vessel occlusion acute ischemic stroke is needed. Here, we report the methods and initial results of the Society of Vascular and Interventional Neurology (SVIN) Registry. Methods The SVIN Registry is an ongoing prospective, multicenter, observational registry capturing patients with large vessel occlusion acute ischemic stroke undergoing endovascular treatment since November 2018. Participating sites also contributed pre‐SVIN Registry data collected per institutional prospective registries, and these data were combined with the SVIN Registry in the SVIN Registry+ cohort. Results There were 2088 patients treated across 11 US centers included in the prospective SVIN Registry and 5372 in SVIN Registry+. In the SVIN Registry cohort, the median number of enrollments per institution was 160 [interquartile range 53–243]. Median age was 67 [58–79] years, 49% were women, median National Institutes of Health Stroke Scale 16 [10–21], Alberta stroke program early CT score 9 [7–10], and 20% had baseline modified Rankin scale (mRS)≥2. The median last‐known normal to puncture time was 7.7 [3.1–11.5] hours, and puncture‐to‐reperfusion was 33 [23–52] minutes. The predominant occlusion site was the middle cerebral artery‐M1 (45%); medium vessel occlusions occurred in 97(4.6%) patients. The median number of passes was 1 [1–3] with 93% achieving expanded Treatment In Cerebral Ischemia2b50–3 reperfusion and 51% expanded Treatment In Cerebral Ischemia3/complete reperfusion. Symptomatic intracranial hemorrhage occurred in 5.3% of patients, with 37.3% functional independence (mRS0–2) and 26.4% mortality rates at 90‐days. Multivariable regression indicated older age, longer last‐normal to reperfusion, higher baseline National Institutes of Health Stroke Scale and glucose, lower Alberta stroke program early CT score, heart failure, and general anesthesia associated with lower 90‐day chances of mRS0–2 at 90‐days. Demographic, imaging, procedural, and clinical outcomes were similar in the SVIN Registry+. A comparison between AHA Guidelines‐eligible patients from the SVIN Registry against the Highly Effective Reperfusion evaluated in Multiple Endovascular Stroke Trials study population demonstrated comparable clinical outcomes. Conclusions The prospective SVIN Registry demonstrates that satisfactory procedural and clinical outcomes can be achieved in real‐world practice, serving as a platform for local quality improvement and the investigation of unexplored frontiers in the endovascular treatment of acute stroke

Machine Learning Automated Detection of Large Vessel Occlusion From Mobile Stroke Unit Computed Tomography Angiography

BACKGROUND: Prehospital automated large vessel occlusion (LVO) detection in Mobile Stroke Units (MSUs) could accelerate identification and treatment of patients with LVO acute ischemic stroke. Here, we evaluate the performance of a machine learning (ML) model on CT angiograms (CTAs) obtained from 2 MSUs to detect LVO. METHODS: Patients evaluated on MSUs in Houston and Los Angeles with out-of-hospital CTAs were identified. Anterior circulation LVO was defined as an occlusion of the intracranial internal carotid artery, middle cerebral artery (M1 or M2), or anterior cerebral artery vessels and determined by an expert human reader. A ML model to detect LVO was trained and tested on independent data sets consisting of in-hospital CTAs and then tested on MSU CTA images. Model performance was determined using area under the receiver-operator curve statistics. RESULTS: Among 68 patients with out-of-hospital MSU CTAs, 40% had an LVO. The most common occlusion location was the middle cerebral artery M1 segment (59%), followed by the internal carotid artery (30%), and middle cerebral artery M2 (11%). Median time from last known well to CTA imaging was 88.0 (interquartile range, 59.5-196.0) minutes. After training on 870 in-hospital CTAs, the ML model performed well in identifying LVO in a separate in-hospital data set of 441 images with area under receiver-operator curve of 0.84 (95% CI, 0.80-0.87). ML algorithm analysis time was under 1 minute. The performance of the ML model on the MSU CTA images was comparable with area under receiver-operator curve 0.80 (95% CI, 0.71-0.89). There was no significant difference in performance between the Houston and Los Angeles MSU CTA cohorts. CONCLUSIONS: In this study of patients evaluated on MSUs in 2 cities, a ML algorithm was able to accurately and rapidly detect LVO using prehospital CTA acquisitions

Automated Large Vessel Occlusion Detection Software and Thrombectomy Treatment Times: a Cluster Randomized Clinical Trial

IMPORTANCE: The benefit of endovascular stroke therapy (EVT) in large vessel occlusion (LVO) ischemic stroke is highly time dependent. Process improvements to accelerate in-hospital workflows are critical. OBJECTIVE: to determine whether automated computed tomography (CT) angiogram interpretation coupled with secure group messaging can improve in-hospital EVT workflows. DESIGN, SETTING, AND PARTICIPANTS: This cluster randomized stepped-wedge clinical trial took place from January 1, 2021, through February 27, 2022, at 4 comprehensive stroke centers (CSCs) in the greater Houston, Texas, area. All 443 participants with LVO stroke who presented through the emergency department were treated with EVT at the 4 CSCs. Exclusion criteria included patients presenting as transfers from an outside hospital (n = 158), in-hospital stroke (n = 39), and patients treated with EVT through randomization in a large core clinical trial (n = 3). INTERVENTION: Artificial intelligence (AI)-enabled automated LVO detection from CT angiogram coupled with secure messaging was activated at the 4 CSCs in a random-stepped fashion. Once activated, clinicians and radiologists received real-time alerts to their mobile phones notifying them of possible LVO within minutes of CT imaging completion. MAIN OUTCOMES AND MEASURES: Primary outcome was the effect of AI-enabled LVO detection on door-to-groin (DTG) time and was measured using a mixed-effects linear regression model, which included a random effect for cluster (CSC) and a fixed effect for exposure status (pre-AI vs post-AI). Secondary outcomes included time from hospital arrival to intravenous tissue plasminogen activator (IV tPA) bolus in eligible patients, time from initiation of CT scan to start of EVT, and hospital length of stay. In exploratory analysis, the study team evaluated the impact of AI implementation on 90-day modified Rankin Scale disability outcomes. RESULTS: Among 243 patients who met inclusion criteria, 140 were treated during the unexposed period and 103 during the exposed period. Median age for the complete cohort was 70 (IQR, 58-79) years and 122 were female (50%). Median National Institutes of Health Stroke Scale score at presentation was 17 (IQR, 11-22) and the median DTG preexposure was 100 (IQR, 81-116) minutes. In mixed-effects linear regression, implementation of the AI algorithm was associated with a reduction in DTG time by 11.2 minutes (95% CI, -18.22 to -4.2). Time from CT scan initiation to EVT start fell by 9.8 minutes (95% CI, -16.9 to -2.6). There were no differences in IV tPA treatment times nor hospital length of stay. In multivariable logistic regression adjusted for age, National Institutes of Health Stroke scale score, and the Alberta Stroke Program Early CT Score, there was no difference in likelihood of functional independence (modified Rankin Scale score, 0-2; odds ratio, 1.3; 95% CI, 0.42-4.0). CONCLUSIONS AND RELEVANCE: Automated LVO detection coupled with secure mobile phone application-based communication improved in-hospital acute ischemic stroke workflows. Software implementation was associated with clinically meaningful reductions in EVT treatment times. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05838456

Automated Large Vessel Occlusion Detection Software and Thrombectomy Treatment Times: A Cluster Randomized Clinical Trial

Importance The benefit of endovascular stroke therapy (EVT) in large vessel occlusion (LVO) ischemic stroke is highly time dependent. Process improvements to accelerate in-hospital workflows are critical. Objective To determine whether automated computed tomography (CT) angiogram interpretation coupled with secure group messaging can improve in-hospital EVT workflows. Design, Setting, and Participants This cluster randomized stepped-wedge clinical trial took place from January 1, 2021, through February 27, 2022, at 4 comprehensive stroke centers (CSCs) in the greater Houston, Texas, area. All 443 participants with LVO stroke who presented through the emergency department were treated with EVT at the 4 CSCs. Exclusion criteria included patients presenting as transfers from an outside hospital (n = 158), in-hospital stroke (n = 39), and patients treated with EVT through randomization in a large core clinical trial (n = 3). Intervention Artificial intelligence (AI)–enabled automated LVO detection from CT angiogram coupled with secure messaging was activated at the 4 CSCs in a random-stepped fashion. Once activated, clinicians and radiologists received real-time alerts to their mobile phones notifying them of possible LVO within minutes of CT imaging completion. Main Outcomes and Measures Primary outcome was the effect of AI-enabled LVO detection on door-to-groin (DTG) time and was measured using a mixed-effects linear regression model, which included a random effect for cluster (CSC) and a fixed effect for exposure status (pre-AI vs post-AI). Secondary outcomes included time from hospital arrival to intravenous tissue plasminogen activator (IV tPA) bolus in eligible patients, time from initiation of CT scan to start of EVT, and hospital length of stay. In exploratory analysis, the study team evaluated the impact of AI implementation on 90-day modified Rankin Scale disability outcomes. Results Among 243 patients who met inclusion criteria, 140 were treated during the unexposed period and 103 during the exposed period. Median age for the complete cohort was 70 (IQR, 58-79) years and 122 were female (50%). Median National Institutes of Health Stroke Scale score at presentation was 17 (IQR, 11-22) and the median DTG preexposure was 100 (IQR, 81-116) minutes. In mixed-effects linear regression, implementation of the AI algorithm was associated with a reduction in DTG time by 11.2 minutes (95% CI, −18.22 to −4.2). Time from CT scan initiation to EVT start fell by 9.8 minutes (95% CI, −16.9 to −2.6). There were no differences in IV tPA treatment times nor hospital length of stay. In multivariable logistic regression adjusted for age, National Institutes of Health Stroke scale score, and the Alberta Stroke Program Early CT Score, there was no difference in likelihood of functional independence (modified Rankin Scale score, 0-2; odds ratio, 1.3; 95% CI, 0.42-4.0). Conclusions and Relevance Automated LVO detection coupled with secure mobile phone application-based communication improved in-hospital acute ischemic stroke workflows. Software implementation was associated with clinically meaningful reductions in EVT treatment times

Endovascular Therapy in the Extended Time Window for Large Vessel Occlusion in Patients With Pre-Stroke Disability.

BACKGROUND AND PURPOSE We compared the outcomes of endovascular therapy (EVT) in an extended time window in patients with large-vessel occlusion (LVO) between patients with and without pre-stroke disability. METHODS In this prespecified analysis of the multinational CT for Late Endovascular Reperfusion study (66 participating sites, 10 countries between 2014 and 2022), we analyzed data from patients with acute ischemic stroke with a pre-stroke modified Rankin Scale (mRS) score of 0-4 and LVO who underwent EVT 6-24 hours from the time last seen well. The primary outcome was the composite of functional independence (FI; mRS score 0-2) or return to the pre-stroke mRS score (return of Rankin, RoR) at 90 days. Outcomes were compared between patients with pre-stroke disability (pre-stroke mRS score 2-4) and those without (mRS score 0-1). RESULTS A total of 2,231 patients (median age, 72 years; median National Institutes of Health Stroke Scale score, 16) were included in the present analysis. Of these, 564 (25%) had pre-stroke disability. The primary outcome (FI or RoR) was observed in 30.7% of patients with pre-stroke disability (FI, 16.5%; RoR, 30.7%) compared to 44.1% of patients without (FI, 44.1%; RoR, 13.0%) (P<0.001). In multivariable logistic regression analysis with inverse probability of treatment weighting, pre-stroke disability was not associated with significantly lower odds of achieving FI or RoR (adjusted odds ratio 0.73, 95% confidence interval 0.43-1.25). Symptomatic intracranial hemorrhage occurred in 6.3% of both groups (P=0.995). CONCLUSION A considerable proportion of patients with late-presenting LVO and pre-stroke disability regained pre-stroke mRS scores after EVT. EVT may be appropriate for patients with pre-stroke disability presenting in the extended time window