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

Chan, N.; Sibtain, N.; Booth, Thomas C.; Souza, P. de; Bibby, S.; Mah, Y.-H.; Teo, J.; U-King-Im, J.M.

doi:10.1016/j.crad.2022.10.007

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…Most of the missed LVOs by the AI-tool included the M2 vessels, which are also more difficult to detect by radiologists. Most other commercially available AI-tools currently on the market do not (yet) detect M2 occlusions [ 18 , 19 ]. Another reason the users may have experienced limited added value is because of their (unconscious) misinterpretation of the intended use.…”

Section: Discussionmentioning

confidence: 99%

AI-support for the detection of intracranial large vessel occlusions: One-year prospective evaluation

Leeuwen

Becks

Grob

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

AI-support for the detection of intracranial large vessel occlusions: One-year prospective evaluation

Leeuwen

Becks

Grob

et al. 2023

Heliyon

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“…Meanwhile, our study is a novel study to mention the limited time for MT. We proved the new potential of ML in the management of AIS [ 35 , 36 , 37 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting Mechanical Thrombectomy Outcome and Time Limit through ADC Value Analysis: A Comprehensive Clinical and Simulation Study Using Machine Learning

et al. 2023

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Predicting outcomes after mechanical thrombectomy (MT) remains challenging for patients with acute ischemic stroke (AIS). This study aimed to explore the usefulness of machine learning (ML) methods using detailed apparent diffusion coefficient (ADC) analysis to predict patient outcomes and simulate the time limit for MT in AIS. A total of 75 consecutive patients with AIS with complete reperfusion in MT were included; 20% were separated to test data. The threshold ranged from 620 × 10−6 mm2/s to 480 × 10−6 mm2/s with a 20 × 10−6 mm2/s step. The mean, standard deviation, and pixel number of the region of interest were obtained according to the threshold. Simulation data were created by mean measurement value of patients with a modified Rankin score of 3–4. The time limit was simulated from the cross point of the prediction score according to the time to perform reperfusion from imaging. The extra tree classifier accurately predicted the outcome (AUC: 0.833. Accuracy: 0.933). In simulation data, the prediction score to obtain a good outcome decreased according to increasing time to reperfusion, and the time limit was longer among younger patients. ML methods using detailed ADC analysis accurately predicted patient outcomes in AIS and simulated tolerance time for MT.

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“…Initially, the manual interpretation of ASPECTS was tedious and often showed variability due to a number of factors including human rater experience (Wardlaw et al, 2007;Menon et al, 2011). Today, the advent of commercially available AI software such as RapidAI's Rapid ASPECTS and Brainomix's e-ASPECTS has automated this process, resulting in improved inter-rater agreement (Nagel et al, 2017;Goebel et al, 2018;Shibata et al, 2022;Chan et al, 2023). Additionally, several studies have produced models that have been able to generate ASPECTS scores from NCCT in high concordance with ASPECTS scores derived from DWI (Albers et al, 2019;Kuang et al, 2019).…”

Section: Prognostication Of Ischemic Strokementioning

confidence: 99%

Advances in Artificial Intelligence, Robotics, Augmented and Virtual Reality in Neurosurgery

Mofatteh,

Bydon,

Ali Alvi

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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

Cited by 7 publications

References 17 publications

AI-support for the detection of intracranial large vessel occlusions: One-year prospective evaluation

AI-support for the detection of intracranial large vessel occlusions: One-year prospective evaluation

Predicting Mechanical Thrombectomy Outcome and Time Limit through ADC Value Analysis: A Comprehensive Clinical and Simulation Study Using Machine Learning

Advances in Artificial Intelligence, Robotics, Augmented and Virtual Reality in Neurosurgery

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