Performance of Automated Attenuation Measurements at Identifying Large Vessel Occlusion Stroke on CT Angiography

Reidler, Paul; Stueckelschweiger, Lena; Puhr‐Westerheide, Daniel; Feil, Katharina; Kellert, Lars; Dimitriadis, Konstantinos; Tiedt, Steffen; Herzberg, Moriz; Rémi, Jan; Liebig, Thomas; Fabritius, Matthias P.; Kunz, Wolfgang G.

doi:10.1007/s00062-020-00956-5

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…In order to provide a full analysis of the capabilities of our algorithm while increasing comparability to previous studies [5][6][7][8][9][10]14,16,[24][25][26][27][28][29][30][31][32][33][34] , the evaluation of the models was divided into (i) object-level and (ii) patient-level evaluation.…”

Section: Discussionmentioning

confidence: 99%

Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke

Brugnara,

Baumgartner,

Scholze

et al. 2023

Nat Commun

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Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute ischemic stroke (AIS), and computer-aided diagnosis (CAD) systems can accelerate the underlying diagnostic processes. Here, we developed an artificial neural network (ANN) which allows automated detection of abnormal vessel findings without any a-priori restrictions and in <2 minutes. Pseudo-prospective external validation was performed in consecutive patients with suspected AIS from 4 different hospitals during a 6-month timeframe and demonstrated high sensitivity (≥87%) and negative predictive value (≥93%). Benchmarking against two CE- and FDA-approved software solutions showed significantly higher performance for our ANN with improvements of 25–45% for sensitivity and 4–11% for NPV (p ≤ 0.003 each). We provide an imaging platform (https://stroke.neuroAI-HD.org) for online processing of medical imaging data with the developed ANN, including provisions for data crowdsourcing, which will allow continuous refinements and serve as a blueprint to build robust and generalizable AI algorithms.

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Section: Discussionmentioning

confidence: 99%

Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke

Brugnara,

Baumgartner,

Scholze

et al. 2023

Nat Commun

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“…Other investigators have found that improving stroke workflow metrics are associated with improved clinical outcomes 15,16 and rate of reperfusion. 17 The use of AI in stroke imaging is an area of active investigation due to the potential for improving diagnostic accuracy, [21][22][23][24][25][26][27] improving outcomes by decreasing time to clinical treatment, 15,16,26 and predicting clinical outcomes. 31,[41][42][43] Given that improvement in time to treatment is associated with improved rate of reperfusion and functional outcomes, these techniques may have high clinical utility, especially in hub/spoke healthcare systems without 24-hour neuroendovascular coverage at most sites.…”

Section: Discussionmentioning

confidence: 99%

“…20 Subsequently, iSchemaView along with other providers have developed new automated techniques for detecting anterior circulation LVO by quantifying relative vessel density in the MCA territory (compared to the contralateral side), including RAPID-CTA (iSchemaView, Menlo Park, California, USA) and Viz.LVO (Viz.ai, San Francisco, California, USA). Many of the studies examining the results of these automated techniques were based on retrospective analyses of datasets from the thrombectomy trials with some patients selected from the general population at academic CSCs; [21][22][23][24][25][26][27] the techniques demonstrated relatively high accuracy, with good results in sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) in that patient population. Few studies looked at their effects on stroke workflow metrics, 26 such as door-togroin puncture or computed tomography angiography (CTA)-to-groin puncture times.…”

Section: Background and Significancementioning

confidence: 99%

Positive predictive value and stroke workflow outcomes using automated vessel density (RAPID-CTA) in stroke patients: One year experience

Adhya

Eisenmenger

et al. 2021

Neuroradiol J

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Purpose Several new techniques have emerged for detecting anterior circulation large vessel occlusion by quantifying relative vessel density including RAPID-CTA, potentially allowing for faster triage and decreased time to mechanical thrombectomy. We present our one-year experience on positive predictive value of RAPID-CTA for the detection of large vessel occlusion in patients presenting with stroke symptoms and its effect on treatment time and clinical outcomes. Materials and methods Three hundred and ten patients presenting with stroke symptoms with relative vessel density <60% on RAPID-CTA were included (average age 70 years, 145 male, 165 female). Examinations were considered positive if there was evidence of large vessel occlusion or high grade stenosis. Computed tomography angiography to groin puncture time was calculated during one-year time intervals before and after RAPID-CTA installation. Ninety-day Modified Rankin Scale scores were obtained for patients in each cohort. Results Of the 310 patients, 270 had large vessel occlusion or high grade stenosis (87% positive predictive value), with 161 having large vessel occlusion. Using 45% relative vessel density threshold, 129/161 large vessel occlusion were detected (80% sensitivity) and 163/172 examinations were positive (95% positive predictive value). Computed tomography angiography to groin puncture time was significantly lower after deployment of RAPID-CTA (93 min vs 68 min, p<0.05). Average 90 day modified Rankin Scale score was lower in the RAPID-CTA group with a higher percentage of patients with functional independence, although the data was not statistically significant. Conclusion RAPID-CTA had high positive predictive value for large vessel occlusion with a 45% relative vessel density threshold, which could facilitate active worklist reprioritization. Time to treatment was significantly lower and clinical outcomes were improved after deployment of RAPID-CTA.

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“…Firstly, Hidlay et al ( 44) used a smartphone-based evaluation of scans for detecting LVO, with sensitivity and specificity of 100% across 80 LVO patients, in a retrospective multicentre study. Next, automated attenuation analysis was trialled by Reidler et al (45) with sensitivity values between 91 and 96% and specificity values between 77 and 83%, for a specificity cutoff of ≥0.70 when applied to their cohort of patients (79 with LVO) (45). The CT-defined hyperdense arterial sign marker for LVO demonstrated reasonably high sensitivity (67%) and specificity (82%) for identifying LVO in ischaemic stroke patients on thin and thick NCCT serial images (48).…”

Section: Imaging and Physiological Monitoring Methodsmentioning

confidence: 99%

Emerging Detection Techniques for Large Vessel Occlusion Stroke: A Scoping Review

et al. 2022

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Background: Large vessel occlusion (LVO) is the obstruction of large, proximal cerebral arteries and can account for up to 46% of acute ischaemic strokes (AIS) when both the A2 and P2 segments are included (from the anterior and posterior cerebral arteries). It is of paramount importance that LVO is promptly recognised to provide timely and effective acute stroke management. This review aims to scope recent literature to identify new emerging detection techniques for LVO. As a good comparator throughout this review, the commonly used National Institutes of Health Stroke Scale (NIHSS), at a cut-off of ≥11, has been reported to have a sensitivity of 86% and a specificity of 60% for LVO.Methods: Four electronic databases (Medline via OVID, CINAHL, Scopus, and Web of Science), and grey literature using OpenGrey, were systematically searched for published literature investigating developments in detection methods for LVO, reported from 2015 to 2021. The protocol for the search was published with the Open Science Framework (10.17605/OSF.IO/A98KN). Two independent researchers screened the titles, abstracts, and full texts of the articles, assessing their eligibility for inclusion.Results: The search identified 5,082 articles, in which 2,265 articles were screened to assess their eligibility. Sixty-two studies remained following full-text screening. LVO detection techniques were categorised into 5 groups: stroke scales (n = 30), imaging and physiological methods (n = 15), algorithmic and machine learning approaches (n = 9), physical symptoms (n = 5), and biomarkers (n = 3).Conclusions: This scoping review has explored literature on novel and advancements in pre-existing detection methods for LVO. The results of this review highlight LVO detection techniques, such as stroke scales and biomarkers, with good sensitivity and specificity performance, whilst also showing advancements to support existing LVO confirmatory methods, such as neuroimaging.

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Performance of Automated Attenuation Measurements at Identifying Large Vessel Occlusion Stroke on CT Angiography

Cited by 8 publications

References 31 publications

Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke

Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke

Positive predictive value and stroke workflow outcomes using automated vessel density (RAPID-CTA) in stroke patients: One year experience

Emerging Detection Techniques for Large Vessel Occlusion Stroke: A Scoping Review

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