Detection of ischemic changes on baseline multimodal computed tomography: expert reading vs. Brainomix and RAPID software

Cimflová, Petra; Volný, Ondřej; Mikulík, Robert; Tyshchenko, Bohdan; Bělašková, Silvie; Vinklárek, Jan; Červeňák, Vladimír; Křivka, Tomáš; Vanicek, Assoc. Prof. Jiri; Krajina, Antonı́n

doi:10.1016/j.jstrokecerebrovasdis.2020.104978

Cited by 6 publications

(6 citation statements)

References 29 publications

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“…A recent study demonstrated that AI/ML prediction of final infarct size is comparable to expert radiologist interpretation. In a cohort of 263 patients presenting with suspected acute ischaemic stroke, of which 83 were included, Cimflova et al found that Brainomix had an accuracy of 75.7% in predicting follow-up ASPECTS score, compared to 79% for expert radiologists and 81.2% for cerebral blood flow (<30% on CT perfusion using RAPID software) 11 . In the present study, RAPID did not have similar success in predicting final infarct size for patients with LVO, but this is limited by the small sample size and further studies could help clarify this.…”

Section: Discussionmentioning

confidence: 99%

Machine-learning algorithm in acute stroke: real-world experience

Chan¹,

Sibtain²,

Booth³

et al. 2023

Clinical Radiology

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Section: Discussionmentioning

confidence: 99%

Machine-learning algorithm in acute stroke: real-world experience

Chan¹,

Sibtain²,

Booth³

et al. 2023

Clinical Radiology

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“…Six studies (median n = 98) include diagnostic accuracy results for e‐ASPECTS with expert reference standards comparable to RITeS: sensitivity 14 to 83%, specificity 57 to 99%, and accuracy 67 to 87%. 21 , 28 , 29 , 30 , 31 , 32 One study used an ASPECTS threshold as we did, 32 the others considered ischemic detection per ASPECTS region for a summed score (ie, 10 × n). However, 2 studies using summed scores did not control for interdependency between different ASPECTS regions in the same patient.…”

Section: Discussionmentioning

confidence: 99%

“…24,25 We did not use concurrent CT perfusion or diffusion-weighted magnetic resonance imaging (MRI) to define a "ground truth." [26][27][28] Therefore, software may identify subtle ischemic injury not appreciated by experts. Indeed, software may be more sensitive than experts (68% vs 58%) for correctly detecting ischemic stroke features.…”

Section: Detection Of Acute Ischemic Injurymentioning

confidence: 99%

External Validation of e‐ASPECTS Software for Interpreting Brain CT in Stroke

Mair

White

Bath

et al. 2022

Annals of Neurology

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Objective: The purpose of this study was to test e-ASPECTS software in patients with stroke. Marketed as a decisionsupport tool, e-ASPECTS may detect features of ischemia or hemorrhage on computed tomography (CT) imaging and quantify ischemic extent using Alberta Stroke Program Early CT Score (ASPECTS). Methods: Using CT from 9 stroke studies, we compared software with masked experts. As per indications for software use, we assessed e-ASPECTS results for patients with/without middle cerebral artery (MCA) ischemia but no other cause of stroke. In an analysis outside the intended use of the software, we enriched our dataset with non-MCA ischemia, hemorrhage, and mimics to simulate a representative "front door" hospital population. With final diagnosis as the reference standard, we tested the diagnostic accuracy of e-ASPECTS for identifying stroke features (ischemia, hyperattenuated arteries, and hemorrhage) in the representative population. Results: We included 4,100 patients (51% women, median age = 78 years, National Institutes of Health Stroke Scale [NIHSS] = 10, onset to scan = 2.5 hours). Final diagnosis was ischemia (78%), hemorrhage (14%), or mimic (8%). From 3,035 CTs with expert-rated ASPECTS, most (2084/3035, 69%) e-ASPECTS results were within one point of experts. In the representative population, the diagnostic accuracy of e-ASPECTS was 71% (95% confidence interval [CI] = 70-72%) for detecting ischemic features, 85% (83-86%) for hemorrhage. Software identified more false positive ischemia (12% vs 2%) and hemorrhage (14% vs <1%) than experts. Interpretation: On independent testing, e-ASPECTS provided moderate agreement with experts and overcalled stroke features. Therefore, future prospective trials testing impacts of artificial intelligence (AI) software on patient care and outcome are required before widespread implementation of stroke decision-support software.

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“…The second cluster ranging from 2019 to 2021 includes publications retrospectively analyzing CTP data from the major clinical trials concomitant with the development of MT, confirming the growing interest in imaging-based patients selection for reperfusion therapies ( 109 – 111 ). This time frame also shows an emergence of automated CTP imaging post-processing to provide target mismatch volume as well as infarct growth prediction ( 112 – 116 ).…”

Section: Penumbra Imaging In the Thrombectomy Eramentioning

confidence: 99%

Clinical Imaging of the Penumbra in Ischemic Stroke: From the Concept to the Era of Mechanical Thrombectomy

Chalet

Boutelier

Christen

et al. 2022

Front. Cardiovasc. Med.

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The ischemic penumbra is defined as the severely hypoperfused, functionally impaired, at-risk but not yet infarcted tissue that will be progressively recruited into the infarct core. Early reperfusion aims to save the ischemic penumbra by preventing infarct core expansion and is the mainstay of acute ischemic stroke therapy. Intravenous thrombolysis and mechanical thrombectomy for selected patients with large vessel occlusion has been shown to improve functional outcome. Given the varying speed of infarct core progression among individuals, a therapeutic window tailored to each patient has recently been proposed. Recent studies have demonstrated that reperfusion therapies are beneficial in patients with a persistent ischemic penumbra, beyond conventional time windows. As a result, mapping the penumbra has become crucial in emergency settings for guiding personalized therapy. The penumbra was first characterized as an area with a reduced cerebral blood flow, increased oxygen extraction fraction and preserved cerebral metabolic rate of oxygen using positron emission tomography (PET) with radiolabeled O2. Because this imaging method is not feasible in an acute clinical setting, the magnetic resonance imaging (MRI) mismatch between perfusion-weighted imaging and diffusion-weighted imaging, as well as computed tomography perfusion have been proposed as surrogate markers to identify the penumbra in acute ischemic stroke patients. Transversal studies comparing PET and MRI or using longitudinal assessment of a limited sample of patients have been used to define perfusion thresholds. However, in the era of mechanical thrombectomy, these thresholds are debatable. Using various MRI methods, the original penumbra definition has recently gained a significant interest. The aim of this review is to provide an overview of the evolution of the ischemic penumbra imaging methods, including their respective strengths and limitations, as well as to map the current intellectual structure of the field using bibliometric analysis and explore future directions.

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Detection of ischemic changes on baseline multimodal computed tomography: expert reading vs. Brainomix and RAPID software

Cited by 6 publications

References 29 publications

Machine-learning algorithm in acute stroke: real-world experience

Machine-learning algorithm in acute stroke: real-world experience

External Validation of e‐ASPECTS Software for Interpreting Brain CT in Stroke

Clinical Imaging of the Penumbra in Ischemic Stroke: From the Concept to the Era of Mechanical Thrombectomy

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