Identification of successful cerebral reperfusions (mTICI ≥2b) using an artificial intelligence strategy

Neves, Gabriel; Warman, Pranav; Bueso, Tulio; Duarte-Celada, Walter R.; Windisch, T.

doi:10.1007/s00234-021-02851-3

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…[8][9][10][11][12] Previous work has attempted to predict reperfusion grading or classify the occlusion as M1 or M2 based on DSA. 13,14 An effective F I G U R E 1 Study workflow. An AP cerebral DSA frame is fed into the model, and if an LVO is present, the overall model will attempt to localize it with a bounding box.…”

Section: Introductionmentioning

confidence: 99%

“…While there are many instances of work using DL strategies to automatically identify and characterize occlusions on CTA, there has been comparably less effort in adopting similar strategies for DSA 8–12 . Previous work has attempted to predict reperfusion grading or classify the occlusion as M1 or M2 based on DSA 13,14 . An effective strategy to precisely localize vessel occlusions in DSAs for intra‐EVT assistance remains to be realized.…”

Section: Introductionmentioning

confidence: 99%

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A deep learning method to identify and localize large‐vessel occlusions from cerebral digital subtraction angiography

Warman,

Warman

et al. 2024

Journal of Neuroimaging

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Background and purposeAn essential step during endovascular thrombectomy is identifying the occluded arterial vessel on a cerebral digital subtraction angiogram (DSA). We developed an algorithm that can detect and localize the position of occlusions in cerebral DSA.MethodsWe retrospectively collected cerebral DSAs from a single institution between 2018 and 2020 from 188 patients, 86 of whom suffered occlusions of the M1 and proximal M2 segments. We trained an ensemble of deep‐learning models on fewer than 60 large‐vessel occlusion (LVO)‐positive patients. We evaluated the model on an independent test set and evaluated the truth of its predicted localizations using Intersection over Union and expert review.ResultsOn an independent test set of 166 cerebral DSA frames with an LVO prevalence of 0.19, the model achieved a specificity of 0.95 (95% confidence interval [CI]: 0.90, 0.99), a precision of 0.7450 (95% CI: 0.64, 0.88), and a sensitivity of 0.76 (95% CI: 0.66, 0.91). The model correctly localized the LVO in at least one frame in 13 of the 14 LVO‐positive patients in the test set. The model achieved a precision of 0.67 (95% CI: 0.52, 0.79), recall of 0.69 (95% CI: 0.46, 0.81), and a mean average precision of 0.75 (95% CI: 0.56, 0.91).ConclusionThis work demonstrates that a deep learning strategy using a limited dataset can generate effective representations used to identify LVOs. Generating an expanded and more complete dataset of LVOs with obstructed LVOs is likely the best way to improve the model's ability to localize LVOs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deep learning method to identify and localize large‐vessel occlusions from cerebral digital subtraction angiography

Warman,

Warman

et al. 2024

Journal of Neuroimaging

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External Validation of an Artificial Intelligence Device for Intracranial Hemorrhage Detection

Neves

Warman²,

Warman³

et al. 2023

World Neurosurgery

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Artificial Intelligence as A Complementary Tool for Clincal Decision-Making in Stroke and Epilepsy

Shah,

Heiss

2024

Brain Sciences

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Neurology is a quickly evolving specialty that requires clinicians to make precise and prompt diagnoses and clinical decisions based on the latest evidence-based medicine practices. In all Neurology subspecialties—Stroke and Epilepsy in particular—clinical decisions affecting patient outcomes depend on neurologists accurately assessing patient disability. Artificial intelligence [AI] can predict the expected neurological impairment from an AIS [Acute Ischemic Stroke], the possibility of ICH [IntraCranial Hemorrhage] expansion, and the clinical outcomes of comatose patients. This review article informs readers of artificial intelligence principles and methods. The article introduces the basic terminology of artificial intelligence before reviewing current and developing AI applications in neurology practice. AI holds promise as a tool to ease a neurologist’s daily workflow and supply unique diagnostic insights by analyzing data simultaneously from several sources, including neurological history and examination, blood and CSF laboratory testing, CNS electrophysiologic evaluations, and CNS imaging studies. AI-based methods are poised to complement the other tools neurologists use to make prompt and precise decisions that lead to favorable patient outcomes.

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Identification of successful cerebral reperfusions (mTICI ≥2b) using an artificial intelligence strategy

Cited by 3 publications

References 33 publications

A deep learning method to identify and localize large‐vessel occlusions from cerebral digital subtraction angiography

A deep learning method to identify and localize large‐vessel occlusions from cerebral digital subtraction angiography

External Validation of an Artificial Intelligence Device for Intracranial Hemorrhage Detection

Artificial Intelligence as A Complementary Tool for Clincal Decision-Making in Stroke and Epilepsy

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