Literature DB >> 31500555

Automated Calculation of Alberta Stroke Program Early CT Score: Validation in Patients With Large Hemispheric Infarct.

Gregory W Albers1, Michael J Wald2, Michael Mlynash1, Juergen Endres3, Roland Bammer4, Matus Straka3, Andreas Maier5, Holly E Hinson6, Kevin N Sheth7, W Taylor Kimberly8, Bradley J Molyneaux9.   

Abstract

Background and Purpose- We compared the Alberta Stroke Program Early CT Score (ASPECTS), calculated using a machine learning-based automatic software tool, RAPID ASPECTS, as well as the median score from 4 experienced readers, with the diffusion-weighted imaging (DWI) ASPECTS obtained following the baseline computed tomography (CT) in patients with large hemispheric infarcts. Methods- CT and magnetic resonance imaging scans from the GAMES-RP study, which enrolled patients with large hemispheric infarctions (82-300 mL) documented on DWI-magnetic resonance imaging, were evaluated by blinded experienced readers to determine both CT and DWI ASPECTS. The CT scans were also evaluated by an automated software program (RAPID ASPECTS). Using the DWI ASPECTS as a reference standard, the median CT ASPECTS of the clinicians and the automated score were compared using the interclass correlation coefficient. Results- The median CT ASPECTS for the clinicians was 5 (interquartile range, 4-7), for RAPID ASPECTS 3 (interquartile range, 1-6), and for DWI ASPECTS 3 (2-4). Median error for RAPID ASPECTS was 1 (interquartile range, -1 to 3) versus 3 (interquartile range, 1-4) for clinicians (P<0.001). The automated score had a higher level of agreement with the median of the DWI ASPECTS, both for the full scale and when dichotomized at <6 versus 6 or more (difference in intraclass correlation coefficient, P=0.001). Conclusions- RAPID ASPECTS was more accurate than experienced clinicians in identifying early evidence of brain ischemia as documented by DWI.

Entities:  

Keywords:  brain; cerebral infarction; machine learning; magnetic resonance imaging; tomography

Mesh:

Year:  2019        PMID: 31500555     DOI: 10.1161/STROKEAHA.119.026430

Source DB:  PubMed          Journal:  Stroke        ISSN: 0039-2499            Impact factor:   7.914


  6 in total

1.  Use of biplane quantitative angiographic imaging with ensemble neural networks to assess reperfusion status during mechanical thrombectomy.

Authors:  Mohammad Mahdi Shiraz Bhurwani; Kenneth V Snyder; Muhammad Waqas; Maxim Mokin; Ryan A Rava; Alexander R Podgorsak; Kelsey N Sommer; Jason M Davies; Elad I Levy; Adnan H Siddiqui; Ciprian N Ionita
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2021-02-15

2.  Post-stroke ASPECTS predicts outcome after thrombectomy.

Authors:  Ronen R Leker; Asaf Honig; Andrei Filioglo; Naaem Simaan; John M Gomori; Jose E Cohen
Journal:  Neuroradiology       Date:  2020-10-06       Impact factor: 2.804

3.  Use of quantitative angiographic methods with a data-driven model to evaluate reperfusion status (mTICI) during thrombectomy.

Authors:  Mohammad Mahdi Shiraz Bhurwani; Kenneth V Snyder; Muhammad Waqas; Maxim Mokin; Ryan A Rava; Alexander R Podgorsak; Felix Chin; Jason M Davies; Elad I Levy; Adnan H Siddiqui; Ciprian N Ionita
Journal:  Neuroradiology       Date:  2021-01-07       Impact factor: 2.995

4.  Novel and Efficient Quantitative Posterior-Circulation-Structure-Based Scale via Noncontrast CT to Predict Ischemic Stroke Prognosis: A Retrospective Study.

Authors:  Wen-Hui Fang; Ying-Chu Chen; Ming-Chen Tsai; Pi-Shao Ko; Ding-Lian Wang; Sui-Lung Su
Journal:  J Pers Med       Date:  2022-01-20

5.  Emerging Artificial Intelligence Imaging Applications for Stroke Interventions.

Authors:  E Lotan
Journal:  AJNR Am J Neuroradiol       Date:  2020-12-31       Impact factor: 3.825

Review 6.  Artificial Intelligence and Acute Stroke Imaging.

Authors:  J E Soun; D S Chow; M Nagamine; R S Takhtawala; C G Filippi; W Yu; P D Chang
Journal:  AJNR Am J Neuroradiol       Date:  2020-11-26       Impact factor: 3.825

  6 in total

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