High-Performance Automated Anterior Circulation CT Angiographic Clot                    Detection in Acute Stroke: A Multireader Comparison

Dehkharghani, Seena; Lansberg, Maarten G; Venkatsubramanian, Chitra; Cereda, Carlo; Lima, Fabrício Oliveira; Coelho, Henrique; Rocha, Felipe de Araújo; Qureshi, Abid; Haerian, Hafez; Mont’alverne, F.; Copeland, Karen A. F.; Heit, Jeremy J

doi:10.1148/radiol.2021202734

Cited by 40 publications

(48 citation statements)

References 27 publications

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“…3 Recent studies have reported performance metrics of other commercially available LVO detection algorithms. For detection of LVO, a sensitivity of 96% and specificity of 98% have been reported for the RAPID-LVO, 12 a sensitivity of 82% and specificity of 90% for Viz LVO, 13 and a sensitivity of 84% and specificity of 96% for e-CTA. 14 However, direct comparisons of performance of these algorithms with the current algorithm are difficult to make due to discrepancies in study design.…”

Section: Discussionmentioning

confidence: 99%

“…Performance per occlusion location stratified by scan acquisition parameters could only be reliably assessed in the MR CLEAN Registry due to the large sample of patients with LVO and heterogeneity of scan protocols used in this cohort. To allow comparison between performance of the current algorithm with those described in prior studies, [12][13][14] we performed a sensitivity analysis in which we excluded patients with M2 occlusions and assessed performance for detection of LVO based on correct identification of the affected hemisphere but not exact occlusion location. Statistical differences in AUC were evaluated using DeLong's method.…”

Section: Discussionmentioning

confidence: 99%

“…These tools are aimed at screening CTAs for LVOs and, in case of a positive finding, notifying not only local radiologists but also the stroke team at the nearest EVT-capable stroke center. [10][11][12][13][14] Determining the performance of such algorithms is needed to estimate their potential clinical utility.…”

Section: Introductionmentioning

confidence: 99%

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Diagnostic performance of an algorithm for automated large vessel occlusion detection on CT angiography

Luijten

Wolff

Duvekot

et al. 2021

J NeuroIntervent Surg

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BackgroundMachine learning algorithms hold the potential to contribute to fast and accurate detection of large vessel occlusion (LVO) in patients with suspected acute ischemic stroke. We assessed the diagnostic performance of an automated LVO detection algorithm on CT angiography (CTA).MethodsData from the MR CLEAN Registry and PRESTO were used including patients with and without LVO. CTA data were analyzed by the algorithm for detection and localization of LVO (intracranial internal carotid artery (ICA)/ICA terminus (ICA-T), M1, or M2). Assessments done by expert neuroradiologists were used as reference. Diagnostic performance was assessed for detection of LVO and per occlusion location by means of sensitivity, specificity, and area under the curve (AUC).ResultsWe analyzed CTAs of 1110 patients from the MR CLEAN Registry (median age (IQR) 71 years (60–80); 584 men; 1110 with LVO) and of 646 patients from PRESTO (median age (IQR) 73 years (62–82); 358 men; 141 with and 505 without LVO). For detection of LVO, the algorithm yielded a sensitivity of 89% in the MR CLEAN Registry and a sensitivity of 72%, specificity of 78%, and AUC of 0.75 in PRESTO. Sensitivity per occlusion location was 88% for ICA/ICA-T, 94% for M1, and 72% for M2 occlusion in the MR CLEAN Registry, and 80% for ICA/ICA-T, 95% for M1, and 49% for M2 occlusion in PRESTO.ConclusionThe algorithm provided a high detection rate for proximal LVO, but performance varied significantly by occlusion location. Detection of M2 occlusion needs further improvement.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Diagnostic performance of an algorithm for automated large vessel occlusion detection on CT angiography

Luijten

Wolff

Duvekot

et al. 2021

J NeuroIntervent Surg

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“…In recent years, artificial intelligence (AI)-driven software has emerged to streamline acute stroke triage by enabling faster diagnosis and treatment time 3–5. Limited literature to date has shown impressive accuracy of AI-based algorithms in detecting LVO stroke on CT angiography (CTA) scans in retrospective cohorts 6–8. Initial real-world application of commercially available AI software has led to faster transfer times between primary and comprehensive stroke centers and more timely and predictable notification of neuroendovascular teams 9 10…”

Section: Introductionmentioning

confidence: 99%

AI software detection of large vessel occlusion stroke on CT angiography: a real-world prospective diagnostic test accuracy study

Matsoukas

Morey

Lock

et al. 2022

J NeuroIntervent Surg

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BackgroundArtificial intelligence (AI) software is increasingly applied in stroke diagnostics. However, the actual performance of AI tools for identifying large vessel occlusion (LVO) stroke in real time in a real-world setting has not been fully studied.ObjectiveTo determine the accuracy of AI software in a real-world, three-tiered multihospital stroke network.MethodsAll consecutive head and neck CT angiography (CTA) scans performed during stroke codes and run through an AI software engine (Viz LVO) between May 2019 and October 2020 were prospectively collected. CTA readings by radiologists served as the clinical reference standard test and Viz LVO output served as the index test. Accuracy metrics were calculated.ResultsOf a total of 1822 CTAs performed, 190 occlusions were identified; 142 of which were internal carotid artery terminus (ICA-T), middle cerebral artery M1, or M2 locations. Accuracy metrics were analyzed for two different groups: ICA-T and M1 ±M2. For the ICA-T/M1 versus the ICA-T/M1/M2 group, sensitivity was 93.8% vs 74.6%, specificity was 91.1% vs 91.1%, negative predictive value was 99.7% vs 97.6%, accuracy was 91.2% vs 89.8%, and area under the curve was 0.95 vs 0.86, respectively. Detection rates for ICA-T, M1, and M2 occlusions were 100%, 93%, and 49%, respectively. As expected, the algorithm offered better detection rates for proximal occlusions than for mid/distal M2 occlusions (58% vs 28%, p=0.03).ConclusionsThese accuracy metrics support Viz LVO as a useful adjunct tool in stroke diagnostics. Fast and accurate diagnosis with high negative predictive value mitigates missing potentially salvageable patients.

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“…However, as reported in similarly designed past studies, a consensus read from three subspecialized neuroradiologists represents a reasonable and generalizable initial experience from which to study the technique. 2,25,26 To limit variability, the current study analyzed exams only at 3T and only using a single high relaxivity contrast agent (gadobutrol). Imaging at lower eld strength or with contrast agents exhibiting weaker relaxation enhancement could impact angiographic quality in non-trivial ways.…”

Section: Discussionmentioning

confidence: 99%

Highly Time-Resolved 4d Mr Angiography Using Golden-Angle Radial Sparse Parallel (Grasp) Mri

Goldman‐Yassen

Raz

Borja

et al. 2022

Preprint

Self Cite

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Current dynamic MRA techniques are limited by temporal resolution and signal-to-noise penalties. GRASP, a fast and flexible MRI technique combining compressed-sensing, parallel imaging, and golden-angle radial sampling, acquires volumetric data continuously and can be reconstructed post hoc for user-defined applications. We describe a custom pipeline to retrospectively reconstruct ultrahigh temporal resolution, dynamic MRA from GRASP imaging obtained in the course of routine practice. GRASP scans were reconstructed using a custom implementation of the GRASP algorithm and post-processed with MeVisLab (MeVis Medical Solutions AG, Germany). Twenty consecutive examinations were scored by three neuroradiologists for angiographic quality of specific vascular segments and imaging artifacts using a 4-point scale. Unsubtracted images, baseline-subtracted images, and a time-domain intensity gradient dataset were available in 2D and 3D reconstructions. Distinct arterial and capillary phases were identified in all reconstructions, median of 2 frames (IQR1-3 and 2-3, respectively). Median rating for vascular segments was 3 (excellent) in all reconstructions and for nearly all segments, with excellent intraclass correlation (range 0.91-1.00). No cases were degraded by artifacts. GRASP-MRI obtained in routine practice can be seamlessly repurposed to produce high quality 4D-MRA with sub-second resolved, isotropic cerebrovascular angiography. Further exploration into diagnostic accuracy in disease-specific applications is warranted.

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High-Performance Automated Anterior Circulation CT Angiographic Clot Detection in Acute Stroke: A Multireader Comparison

Cited by 40 publications

References 27 publications

Diagnostic performance of an algorithm for automated large vessel occlusion detection on CT angiography

Diagnostic performance of an algorithm for automated large vessel occlusion detection on CT angiography

AI software detection of large vessel occlusion stroke on CT angiography: a real-world prospective diagnostic test accuracy study

Highly Time-Resolved 4d Mr Angiography Using Golden-Angle Radial Sparse Parallel (Grasp) Mri

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