J 2020

Validation of an automated ASPECTS method on non-contrast computed tomography scans of acute ischemic stroke patients

KUANG, H. L., W. QIU, M. NAJM, D. DOWLATSHAHI, Robert MIKULÍK et. al.

Basic information

Original name

Validation of an automated ASPECTS method on non-contrast computed tomography scans of acute ischemic stroke patients

Authors

KUANG, H. L. (124 Canada), W. QIU (124 Canada), M. NAJM (124 Canada), D. DOWLATSHAHI (124 Canada), Robert MIKULÍK (203 Czech Republic, belonging to the institution), A. Y. POPPE (124 Canada), J. PUIG (724 Spain), M. CASTELLANOS (724 Spain), S. I. SOHN (410 Republic of Korea), S. H. AHN (410 Republic of Korea), A. CALLEJA (724 Spain), A. JIN (124 Canada), T. ASIL (792 Turkey), N. ASDAGHI (840 United States of America), T. S. FIELD (124 Canada), S. COUTTS (124 Canada), M. D. HILL (124 Canada), A. M. DEMCHUK (124 Canada), M. GOYAL (124 Canada) and B. K. MENON (124 Canada, guarantor)

Edition

International Journal of Stroke, London, Sage, 2020, 1747-4930

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30103 Neurosciences

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 5.266

RIV identification code

RIV/00216224:14110/20:00116153

Organization unit

Faculty of Medicine

DOI

UT WoS

000503584300001

Keywords in English

Alberta Stroke Program Early CT score; non-contrast computed tomography; ischemic stroke; machine learning; automated ASPECTS scoring

Tags

Tags

International impact, Reviewed
Změněno: 10/8/2020 08:05, Mgr. Tereza Miškechová

Abstract

V originále

Background The Alberta Stroke Program Early CT Score (ASPECTS) is a systematic method of assessing the extent of early ischemic change on non-contrast computed tomography in patients with acute ischemic stroke. Our objective was to validate an automated ASPECTS scoring method we recently developed on a large data set. Materials and methods We retrospectively collected 602 acute ischemic stroke patients' non-contrast computed tomography scans. Expert ASPECTS readings on non-contrast computed tomography were compared to automated ASPECTS. Statistical analyses on the total ASPECTS, region level ASPECTS, and dichotomized ASPECTS (<= 4 vs. >4) score were conducted. Results In total, 602 scans were evaluated and 6020 (602 x 10) ASPECTS regions were scored. Median time from stroke onset to computed tomography was 114 min (interquartile range: 73-183 min). Total ASPECTS for the 602 patients generated by the automated method agreed well with expert readings (intraclass correlation coefficient): 0.65 (95% confidence interval (CI): 0.60-0.69). Region level analysis showed that the automated method yielded accuracy of 81.25%, sensitivity of 61.13% (95% CI: 58.4%-63.8%), specificity of 86.56% (95% CI: 85.6%-87.5%), and area under curve of 0.74 (95% CI: 0.73-0.75). For dichotomized ASPECTS (<= 4 vs. >4), the automated method demonstrated sensitivity 97.21% (95% CI: 95.4%-98.4%), specificity 57.81% (95% CI: 44.8%-70.1%), accuracy 93.02%, and area under the curve of 0.78 (95% CI: 0.74-0.81). For each individual region (M1-6, lentiform, insula, and caudate), the automated method demonstrated acceptable performance. Conclusion The automated system we developed approached the stroke expert in performance when scoring ASPECTS on non-contrast computed tomography scans of acute ischemic stroke patients.