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brainomix e stroke e aspects software  (Brainomix)

 
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    Brainomix brainomix e stroke e aspects software
    Brainomix E Stroke E Aspects Software, supplied by Brainomix, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/brainomix+e+aspects+software/pmc12738961-53-10-10?v=Brainomix
    Average 86 stars, based on 1 article reviews
    brainomix e stroke e aspects software - by Bioz Stars, 2026-07
    86/100 stars

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    Image Search Results


    Workflow of the clinical test, including data collection. Flowchart shows patient selection with criteria and workflow to the clinical test for Heuron ASPECTS evaluation.

    Journal: Journal of Neurointerventional Surgery

    Article Title: Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT

    doi: 10.1136/jnis-2022-019970

    Figure Lengend Snippet: Workflow of the clinical test, including data collection. Flowchart shows patient selection with criteria and workflow to the clinical test for Heuron ASPECTS evaluation.

    Article Snippet: A previous study reported a limited correlation between automated ASPECTS software (Brainomix e-ASPECTS, RAPID ASPECTS, and Frontier V2 [Siemens Healthcare GmbH, Forchheim, Germany]) and expert consensus, especially for the M3 segment (AUC, −0.027–0.693) and internal capsule (AUC, 0.000–0.691).

    Techniques: Selection

    The automatic ASPECTS estimation based on the deep-learning model and its performance. (A) progress in estimating the aspects, and (B) output of Heuron ASPECTS. The study results are shown as a Bland–Altman plot (C) of experts’ consensus and Heuron ASPECTS. The mean difference is 0.03, and the upper and lower limits of agreement are 2.80 and −2.74, respectively, satisfying prespecified primary outcomes. (D) The intraclass correlation coefficient (ICC) is 0.78 (95% CI: 0.73 to 0.83), showing good to excellent agreement.

    Journal: Journal of Neurointerventional Surgery

    Article Title: Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT

    doi: 10.1136/jnis-2022-019970

    Figure Lengend Snippet: The automatic ASPECTS estimation based on the deep-learning model and its performance. (A) progress in estimating the aspects, and (B) output of Heuron ASPECTS. The study results are shown as a Bland–Altman plot (C) of experts’ consensus and Heuron ASPECTS. The mean difference is 0.03, and the upper and lower limits of agreement are 2.80 and −2.74, respectively, satisfying prespecified primary outcomes. (D) The intraclass correlation coefficient (ICC) is 0.78 (95% CI: 0.73 to 0.83), showing good to excellent agreement.

    Article Snippet: A previous study reported a limited correlation between automated ASPECTS software (Brainomix e-ASPECTS, RAPID ASPECTS, and Frontier V2 [Siemens Healthcare GmbH, Forchheim, Germany]) and expert consensus, especially for the M3 segment (AUC, −0.027–0.693) and internal capsule (AUC, 0.000–0.691).

    Techniques:

    Sensitivity, specificity, and AUC of Heuron ASPECTS prediction in comparison with experts’ consensus in each region of interest and dichotomized cut-off points

    Journal: Journal of Neurointerventional Surgery

    Article Title: Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT

    doi: 10.1136/jnis-2022-019970

    Figure Lengend Snippet: Sensitivity, specificity, and AUC of Heuron ASPECTS prediction in comparison with experts’ consensus in each region of interest and dichotomized cut-off points

    Article Snippet: A previous study reported a limited correlation between automated ASPECTS software (Brainomix e-ASPECTS, RAPID ASPECTS, and Frontier V2 [Siemens Healthcare GmbH, Forchheim, Germany]) and expert consensus, especially for the M3 segment (AUC, −0.027–0.693) and internal capsule (AUC, 0.000–0.691).

    Techniques: Comparison