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Predicting DWI-FLAIR mismatch on NCCT: the role of artificial intelligence in hyperacute decision making

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F23%3A00078764" target="_blank" >RIV/00159816:_____/23:00078764 - isvavai.cz</a>

  • Alternative codes found

    RIV/00216224:14110/23:00133283

  • Result on the web

    <a href="https://www.frontiersin.org/articles/10.3389/fneur.2023.1201223/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/fneur.2023.1201223/full</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3389/fneur.2023.1201223" target="_blank" >10.3389/fneur.2023.1201223</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Predicting DWI-FLAIR mismatch on NCCT: the role of artificial intelligence in hyperacute decision making

  • Original language description

    BackgroundThe presence of diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) mismatch was used to determine eligibility for intravenous thrombolysis in clinical trials. However, due to the restricted availability of MRI and the ambiguity of image assessment, it is not widely implemented in clinical practice. MethodsA total of 222 acute ischemic stroke patients underwent non-contrast computed tomography (NCCT), DWI, and FLAIR within 1 h of one another. Human experts manually segmented ischemic lesions on DWI and FLAIR images and independently graded the presence of DWI-FLAIR mismatch. Deep learning (DL) models based on the nnU-net architecture were developed to predict ischemic lesions visible on DWI and FLAIR images using NCCT images. Inexperienced neurologists evaluated the DWI-FLAIR mismatch on NCCT images without and with the model&apos;s results. ResultsThe mean age of included subjects was 71.8 &amp; PLUSMN; 12.8 years, 123 (55%) were male, and the baseline NIHSS score was a median of 11 [IQR, 6-18]. All images were taken in the following order: NCCT - DWI - FLAIR, starting after a median of 139 [81-326] min after the time of the last known well. Intravenous thrombolysis was administered in 120 patients (54%) after NCCT. The DL model&apos;s prediction on NCCT images revealed a Dice coefficient and volume correlation of 39.1% and 0.76 for DWI lesions and 18.9% and 0.61 for FLAIR lesions. In the subgroup with 15 mL or greater lesion volume, the evaluation of DWI-FLAIR mismatch from NCCT by inexperienced neurologists improved in accuracy (from 0.537 to 0.610) and AUC-ROC (from 0.493 to 0.613). ConclusionThe DWI-FLAIR mismatch may be reckoned using NCCT images through advanced artificial intelligence techniques.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    30210 - Clinical neurology

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Frontiers in Neurology

  • ISSN

    1664-2295

  • e-ISSN

  • Volume of the periodical

    14

  • Issue of the periodical within the volume

    JUN 12

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    10

  • Pages from-to

    1201223

  • UT code for WoS article

    001013166500001

  • EID of the result in the Scopus database

    2-s2.0-85163631325