Literature DB >> 26880058

Performance of e-ASPECTS software in comparison to that of stroke physicians on assessing CT scans of acute ischemic stroke patients.

Christian Herweh1, Peter A Ringleb2, Geraldine Rauch3, Steven Gerry4, Lars Behrens1, Markus Möhlenbruch1, Rebecca Gottorf2, Daniel Richter2, Simon Schieber2, Simon Nagel5.   

Abstract

BACKGROUND: The Alberta Stroke Program Early CT score (ASPECTS) is an established 10-point quantitative topographic computed tomography scan score to assess early ischemic changes. We compared the performance of the e-ASPECTS software with those of stroke physicians at different professional levels.
METHODS: The baseline computed tomography scans of acute stroke patients, in whom computed tomography and diffusion-weighted imaging scans were obtained less than two hours apart, were retrospectively scored by e-ASPECTS as well as by three stroke experts and three neurology trainees blinded to any clinical information. The ground truth was defined as the ASPECTS on diffusion-weighted imaging scored by another two non-blinded independent experts on consensus basis. Sensitivity and specificity in an ASPECTS region-based and an ASPECTS score-based analysis as well as receiver-operating characteristic curves, Bland-Altman plots with mean score error, and Matthews correlation coefficients were calculated. Comparisons were made between the human scorers and e-ASPECTS with diffusion-weighted imaging being the ground truth. Two methods for clustered data were used to estimate sensitivity and specificity in the region-based analysis.
RESULTS: In total, 34 patients were included and 680 (34 × 20) ASPECTS regions were scored. Mean time from onset to computed tomography was 172 ± 135 min and mean time difference between computed tomographyand magnetic resonance imaging was 41 ± 31 min. The region-based sensitivity (46.46% [CI: 30.8;62.1]) of e-ASPECTS was better than three trainees and one expert (p ≤ 0.01) and not statistically different from another two experts. Specificity (94.15% [CI: 91.7;96.6]) was lower than one expert and one trainee (p < 0.01) and not statistically different to the other four physicians. e-ASPECTS had the best Matthews correlation coefficient of 0.44 (experts: 0.38 ± 0.08 and trainees: 0.19 ± 0.05) and the lowest mean score error of 0.56 (experts: 1.44 ± 1.79 and trainees: 1.97 ± 2.12).
CONCLUSION: e-ASPECTS showed a similar performance to that of stroke experts in the assessment of brain computed tomographys of acute ischemic stroke patients with the Alberta Stroke Program Early CT score method.
© 2016 World Stroke Organization.

Entities:  

Keywords:  Alberta Stroke Program Early CT score; computed tomography; ischemic stroke; machine learning

Mesh:

Year:  2016        PMID: 26880058     DOI: 10.1177/1747493016632244

Source DB:  PubMed          Journal:  Int J Stroke        ISSN: 1747-4930            Impact factor:   5.266


  24 in total

1.  Innovative use of artificial intelligence and digital communication in acute stroke pathway in response to COVID-19.

Authors:  Kiruba Nagaratnam; George Harston; Enrico Flossmann; Clara Canavan; Rui Carmelo Geraldes; Chani Edwards
Journal:  Future Healthc J       Date:  2020-06

Review 2.  Current Applications and Future Impact of Machine Learning in Radiology.

Authors:  Garry Choy; Omid Khalilzadeh; Mark Michalski; Synho Do; Anthony E Samir; Oleg S Pianykh; J Raymond Geis; Pari V Pandharipande; James A Brink; Keith J Dreyer
Journal:  Radiology       Date:  2018-06-26       Impact factor: 11.105

3.  Automated ASPECT rating: comparison between the Frontier ASPECT Score software and the Brainomix software.

Authors:  Juliane Goebel; Elena Stenzel; Nika Guberina; Isabel Wanke; Martin Koehrmann; Christoph Kleinschnitz; Lale Umutlu; Michael Forsting; Christoph Moenninghoff; Alexander Radbruch
Journal:  Neuroradiology       Date:  2018-09-15       Impact factor: 2.804

4.  Detection of early infarction signs with machine learning-based diagnosis by means of the Alberta Stroke Program Early CT score (ASPECTS) in the clinical routine.

Authors:  Nika Guberina; U Dietrich; A Radbruch; J Goebel; C Deuschl; A Ringelstein; M Köhrmann; C Kleinschnitz; M Forsting; C Mönninghoff
Journal:  Neuroradiology       Date:  2018-07-31       Impact factor: 2.804

Review 5.  [Imaging in acute ischemic stroke using automated postprocessing algorithms].

Authors:  K Egger; C Strecker; E Kellner; H Urbach
Journal:  Nervenarzt       Date:  2018-08       Impact factor: 1.214

6.  Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning.

Authors:  H Kuang; M Najm; D Chakraborty; N Maraj; S I Sohn; M Goyal; M D Hill; A M Demchuk; B K Menon; W Qiu
Journal:  AJNR Am J Neuroradiol       Date:  2018-11-29       Impact factor: 3.825

7.  Automated versus manual imaging assessment of early ischemic changes in acute stroke: comparison of two software packages and expert consensus.

Authors:  Friederike Austein; Fritz Wodarg; Nora Jürgensen; Monika Huhndorf; Johannes Meyne; Thomas Lindner; Olav Jansen; Naomi Larsen; Christian Riedel
Journal:  Eur Radiol       Date:  2019-05-10       Impact factor: 5.315

8.  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

9.  Collateral Automation for Triage in Stroke: Evaluating Automated Scoring of Collaterals in Acute Stroke on Computed Tomography Scans.

Authors:  Iris Q Grunwald; Johann Kulikovski; Wolfgang Reith; Stephen Gerry; Rafael Namias; Maria Politi; Panagiotis Papanagiotou; Marco Essig; Shrey Mathur; Olivier Joly; Khawar Hussain; Viola Wagner; Sweni Shah; George Harston; Julija Vlahovic; Silke Walter; Anna Podlasek; Klaus Fassbender
Journal:  Cerebrovasc Dis       Date:  2019-06-19       Impact factor: 2.762

10.  e-ASPECTS software improves interobserver agreement and accuracy of interpretation of aspects score.

Authors:  Waleed Brinjikji; Mehdi Abbasi; Catherine Arnold; John C Benson; Sherry A Braksick; Norbert Campeau; Carrie M Carr; Petrice M Cogswell; James P Klaas; Greta B Liebo; Jason T Little; Patrick H Luetmer; Steven A Messina; Alex A Nagelschneider; Kara M Schwartz; Christopher P Wood; Deena M Nasr; David F Kallmes
Journal:  Interv Neuroradiol       Date:  2021-04-14       Impact factor: 1.610

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