Literature DB >> 30720400

Automated Calculation of the Alberta Stroke Program Early CT Score: Feasibility and Reliability.

Christian Maegerlein1, Johanna Fischer1, Sebastian Mönch1, Maria Berndt1, Silke Wunderlich1, Christian L Seifert1, Manuel Lehm1, Tobias Boeckh-Behrens1, Claus Zimmer1, Benjamin Friedrich1.   

Abstract

Background The Alberta Stroke Program Early CT Score (ASPECTS) evaluation is a qualitative method to evaluate focal hypoattenuation at brain CT in early acute stroke. However, interobserver agreement is only moderate. Purpose To compare ASPECTS calculated by using an automatic software tool to neuroradiologist evaluation in the setting of acute stroke. Materials and Methods For this retrospective study, consensus ASPECTS were defined by two neuroradiologists based on baseline noncontrast CTs collected from January 2017 to December 2017 from patients with an occlusion in the middle cerebral artery and from an additional cohort of patients suspected of having stroke and no large vessel occlusion. Imaging data from both baseline and follow-up CT was evaluated for the consensus reading. After 6 weeks, the same two neuroradiologists again determined ASPECTS by using only the baseline CT. For comparison, ASPECTS was also calculated from baseline CT images by using a commercially available software (RAPID ASPECTS). Both methods were compared by using weighted κ statistics. Results CT scans from 100 patients with middle cerebral artery occlusion (44 women [mean age ± standard deviation, 75 years ± 14] and 56 men [mean age, 71 years ± 14]) and 52 patients suspected of having stroke and no large vessel occlusion (19 women [mean age, 69 years ± 18] and 33 men [68 years ± 15]) were evaluated. Neuroradiologists showed moderate agreement with the consensus score (κ = 0.57 and κ = 0.56). Software analysis showed substantial agreement (κ = 0.9) with the consensus score. Software analysis showed a substantial agreement (κ = 0.78) after greater than 1 hour between symptom onset and imaging, which increased to high agreement (κ = 0.92) in the time window greater than 4 hours. The neuroradiologist raters did not achieve comparable results to the software until the time interval of greater than 4 hours (κ = 0.83 and κ = 0.76). Conclusion In acute stroke of the middle cerebral artery, the Alberta Stroke Program Early CT score calculated with automated software had better agreement than that of human readers with a predefined consensus score. © RSNA, 2019 Online supplemental material is available for this article.

Entities:  

Mesh:

Year:  2019        PMID: 30720400     DOI: 10.1148/radiol.2019181228

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  14 in total

1.  Automated ASPECTS in Acute Ischemic Stroke: A Comparative Analysis with CT Perfusion.

Authors:  V K Sundaram; J Goldstein; D Wheelwright; A Aggarwal; P S Pawha; A Doshi; J T Fifi; R De Leacy; J Mocco; J Puig; K Nael
Journal:  AJNR Am J Neuroradiol       Date:  2019-11-14       Impact factor: 3.825

2.  Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging.

Authors:  Manoj Mannil; Nicolin Hainc; Risto Grkovski; Sebastian Winklhofer
Journal:  Acta Neurochir Suppl       Date:  2022

3.  Artificial Intelligence in "Code Stroke"-A Paradigm Shift: Do Radiologists Need to Change Their Practice?

Authors:  Achala Vagal; Luca Saba
Journal:  Radiol Artif Intell       Date:  2022-01-19

Review 4.  Preprocedural Imaging : A Review of Different Radiological Factors Affecting the Outcome of Thrombectomy.

Authors:  Mingxue Jing; Joshua Y P Yeo; Staffan Holmin; Tommy Andersson; Fabian Arnberg; Paul Bhogal; Cunli Yang; Anil Gopinathan; Tian Ming Tu; Benjamin Yong Qiang Tan; Ching Hui Sia; Hock Luen Teoh; Prakash R Paliwal; Bernard P L Chan; Vijay Sharma; Leonard L L Yeo
Journal:  Clin Neuroradiol       Date:  2021-10-28       Impact factor: 3.649

5.  Navigating Supply Chain Disruptions of Iodinated Contrast Agent for Neuroimaging and How Business Intelligence Can Help the Decision Process.

Authors:  R Bammer; S A Amukotuwa
Journal:  AJNR Am J Neuroradiol       Date:  2022-06-01       Impact factor: 4.966

6.  Rapid Assessment of Acute Ischemic Stroke by Computed Tomography Using Deep Convolutional Neural Networks.

Authors:  Peng-Hsiang Hung; Daw-Tung Lin; Chung-Ming Lo
Journal:  J Digit Imaging       Date:  2021-05-07       Impact factor: 4.903

Review 7.  Computer-aided imaging analysis in acute ischemic stroke - background and clinical applications.

Authors:  Yahia Mokli; Johannes Pfaff; Daniel Pinto Dos Santos; Christian Herweh; Simon Nagel
Journal:  Neurol Res Pract       Date:  2019-08-15

Review 8.  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

9.  Posterior circulation stroke: machine learning-based detection of early ischemic changes in acute non-contrast CT scans.

Authors:  Helge C Kniep; Peter B Sporns; Gabriel Broocks; André Kemmling; Jawed Nawabi; Thilo Rusche; Jens Fiehler; Uta Hanning
Journal:  J Neurol       Date:  2020-05-11       Impact factor: 4.849

10.  Eosinophil-to-Monocyte Ratio is a Potential Predictor of Prognosis in Acute Ischemic Stroke Patients After Intravenous Thrombolysis.

Authors:  Yueping Chen; Junli Ren; Naiping Yang; Honghao Huang; Xueting Hu; Fangyue Sun; Tian Zeng; Xinbo Zhou; Wenjing Pan; Jingyu Hu; Beibei Gao; Shunkai Zhang; Guangyong Chen
Journal:  Clin Interv Aging       Date:  2021-05-17       Impact factor: 4.458

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.