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

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.

Základní údaje

Originální název

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

Autoři

Vydání

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

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30103 Neurosciences

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 5.266

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14110/20:00116153

Organizační jednotka

Lékařská fakulta

Klíčová slova anglicky

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

Štítky

14110127, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 10. 8. 2020 08:05, Mgr. Tereza Miškechová

Anotace

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.

Citovat

KUANG, H. L.; W. QIU; M. NAJM; D. DOWLATSHAHI; Robert MIKULÍK; A. Y. POPPE; J. PUIG; M. CASTELLANOS; S. I. SOHN; S. H. AHN; A. CALLEJA; A. JIN; T. ASIL; N. ASDAGHI; T. S. FIELD; S. COUTTS; M. D. HILL; A. M. DEMCHUK; M. GOYAL a B. K. MENON. Validation of an automated ASPECTS method on non-contrast computed tomography scans of acute ischemic stroke patients. International Journal of Stroke. London: Sage, 2020, roč. 15, č. 5, s. 528-534. ISSN 1747-4930. Dostupné z: https://doi.org/10.1177/1747493019895702.

@article{1673716,
   author = {Kuang, H. L. and Qiu, W. and Najm, M. and Dowlatshahi, D. and Mikulík, Robert and Poppe, A. Y. and Puig, J. and Castellanos, M. and Sohn, S. I. and Ahn, S. H. and Calleja, A. and Jin, A. and Asil, T. and Asdaghi, N. and Field, T. S. and Coutts, S. and Hill, M. D. and Demchuk, A. M. and Goyal, M. and Menon, B. K.},
   article_location = {London},
   article_number = {5},
   doi = {https://doi.org/10.1177/1747493019895702},
   keywords = {Alberta Stroke Program Early CT score; non-contrast computed tomography; ischemic stroke; machine learning; automated ASPECTS scoring},
   language = {eng},
   issn = {1747-4930},
   journal = {International Journal of Stroke},
   title = {Validation of an automated ASPECTS method on non-contrast computed tomography scans of acute ischemic stroke patients},
   url = {https://journals.sagepub.com/doi/10.1177/1747493019895702},
   volume = {15},
   year = {2020}
}

TY  - JOUR
ID  - 1673716
AU  - Kuang, H. L. - Qiu, W. - Najm, M. - Dowlatshahi, D. - Mikulík, Robert - Poppe, A. Y. - Puig, J. - Castellanos, M. - Sohn, S. I. - Ahn, S. H. - Calleja, A. - Jin, A. - Asil, T. - Asdaghi, N. - Field, T. S. - Coutts, S. - Hill, M. D. - Demchuk, A. M. - Goyal, M. - Menon, B. K.
PY  - 2020
TI  - Validation of an automated ASPECTS method on non-contrast computed tomography scans of acute ischemic stroke patients
JF  - International Journal of Stroke
VL  - 15
IS  - 5
SP  - 528-534
EP  - 528-534
PB  - Sage
SN  - 17474930
KW  - Alberta Stroke Program Early CT score
KW  - non-contrast computed tomography
KW  - ischemic stroke
KW  - machine learning
KW  - automated ASPECTS scoring
UR  - https://journals.sagepub.com/doi/10.1177/1747493019895702
L2  - https://journals.sagepub.com/doi/10.1177/1747493019895702
N2  - 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.
ER  -

KUANG, H. L.; W. QIU; M. NAJM; D. DOWLATSHAHI; Robert MIKULÍK; A. Y. POPPE; J. PUIG; M. CASTELLANOS; S. I. SOHN; S. H. AHN; A. CALLEJA; A. JIN; T. ASIL; N. ASDAGHI; T. S. FIELD; S. COUTTS; M. D. HILL; A. M. DEMCHUK; M. GOYAL a B. K. MENON. Validation of an automated ASPECTS method on non-contrast computed tomography scans of acute ischemic stroke patients. \textit{International Journal of Stroke}. London: Sage, 2020, roč.~15, č.~5, s.~528-534. ISSN~1747-4930. Dostupné z: https://doi.org/10.1177/1747493019895702.

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