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Original research
Artificial intelligence-driven ASPECTS for the detection of early stroke changes in non-contrast CT: a systematic review and meta-analysis
  1. Antonis Adamou1,
  2. Eleftherios T Beltsios2,
  3. Angelina Bania3,
  4. Androniki Gkana4,
  5. Andreas Kastrup5,
  6. Achilles Chatziioannou6,
  7. Maria Politi7,8,
  8. Panagiotis Papanagiotou6,8
  1. 1Department of Radiology, University of Thessaly, School of Health Sciences, Larissa, Greece
  2. 2Department of Thoracic and Cardiovascular Surgery, West German Heart and Vascular Center, University of Duisburg-Essen, Essen, Germany
  3. 3Faculty of Medicine, University of Patras, School of Health Sciences, Patras, Greece
  4. 4Deparment of Radiology, Ippokratio Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
  5. 5Department of Neurology, Hospital Bremen-Mitte GmbH, Bremen, Germany
  6. 6Department of Radiology, Areteion University Hospital, National and Kapodistrian University of Athens, Athens, Greece
  7. 7Interventional Radiology Unit, Evangelismos General Hospital, Athens, Greece
  8. 8Department of Diagnostic and Interventional Neuroradiology, Hospital Bremen-Mitte GmbH, Bremen, Germany
  1. Correspondence to Professor Panagiotis Papanagiotou, Hospital Bremen-Mitte gGmbH, Bremen, Germany; papanagiotou{at}me.com

Abstract

Background Recent advances in machine learning have enabled development of the automated Alberta Stroke Program Early CT Score (ASPECTS) prediction algorithms using non-contrast enhanced computed tomography (NCCT) scans. The applicability of automated ASPECTS in daily clinical practice is yet to be established. The objective of this meta-analysis was to directly compare the performance of automated and manual ASPECTS predictions in recognizing early stroke changes on NCCT.

Methods The MEDLINE, Scopus, and Cochrane databases were searched. The last database search was performed on March 10, 2022. Studies reporting the diagnostic performance and validity of automated ASPECTS software compared with expert readers were included. The outcomes were the interobserver reliability of outputs between ASPECTS versus expert readings, experts versus reference standard, and ASPECTS versus reference standard by means of pooled Fisher’s Z transformation of the interclass correlation coefficients (ICCs).

Results Eleven studies were included in the meta-analysis, involving 1976 patients. The meta-analyses showed good interobserver reliability between experts (ICC 0.72 (95% CI 0.63 to 0.79); p<0.001), moderate reliability in the correlation between automated and expert readings (ICC 0.54 (95% CI 0.40 to 0.67); p<0.001), good reliability between the total expert readings and the reference standard (ICC 0.62 (95% CI 0.52 to 0.71); p<0.001), and good reliability between the automated predictions and the reference standard (ICC 0.72 (95% CI 0.61 to 0.80); p<0.001).

Conclusions Artificial intelligence-driven ASPECTS software has comparable or better performance than physicians in terms of recognizing early stroke changes on NCCT.

  • Stroke
  • Thrombectomy
  • CT
  • Technology
  • Technique

Data availability statement

Data are available upon reasonable request. Not applicable.

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Data availability statement

Data are available upon reasonable request. Not applicable.

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Footnotes

  • Contributors Conceptualization: PP. Methodology: AA, ETB, MP and PP. Software and formal analysis: AA, ETB, AB, AG. Resources, AA. Data collection: AA, ETB, AB and AG. Data curation: AA. Writing—original draft preparation: AA and ETB. Writing—review and editing: AK, AC, MP and PP. Visualization: AA, AB and AG. Supervision: PP. Project administration: AA and PP. Guarantor: PP. All authors have read and have agreed to the published version of the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.