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


Kuang H., Qiu W., Najm M., Dowlatshahi D., Mikulik R., Poppe A. Y., ...More

INTERNATIONAL JOURNAL OF STROKE, vol.15, no.5, pp.528-534, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 15 Issue: 5
  • Publication Date: 2020
  • Doi Number: 10.1177/1747493019895702
  • Journal Name: INTERNATIONAL JOURNAL OF STROKE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, EMBASE, MEDLINE
  • Page Numbers: pp.528-534
  • Bezmialem Vakıf University Affiliated: Yes

Abstract

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.