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. Author manuscript; available in PMC: 2022 Jun 7.
Published in final edited form as: World Neurosurg. 2021 Dec 8;159:207–220.e1. doi: 10.1016/j.wneu.2021.12.004

Table 3.

Included Studies Regarding Alberta Stroke Program Early CT Score and Patient Selection

Category Reference Country (Income Status) Quality Grade Risk of Bias Concern for Applicability Artificial Intelligence Techniques Key Findings
ASPECTS Grunwald et al., 201941 Germany, United Kingdom (high) Moderate Unclear Low NA Addition of e-CTA improved intraclass correlation coefficient among neuroradiologists Automated e-CTA, without neuroradiologist input, agreed with consensus score in 90% of scans with remaining 10% within 1 point of the consensus
Sensitivity of 0.99 and specificity of 0.94 for identifying favorable collateral flow (collateral score 2–3)
Guberina et al., 201842 Germany (high) Moderate Unclear Low NA Higher interrater correlation coefficient of 0.71–0.80 with definite infarct core, compared with 0.59 for ASPECTS in the acute ischemic stroke setting
Nagel et al., 201650 Canada, Germany, United Kingdom, United States (high) Moderate Unclear Low NA Two e-ASPECTS operating points were noninferior to all 3 neuroradiologists Matthews correlation coefficients for e-ASPECTS were higher than those of all neuroradiologists
Neuhaus et al., 202051 United States, United Kingdom (high) Moderate Low Low NA Median ASPECTS was 9 for manual scoring and 8.5 for e-ASPECTS with κ of 0.248 When corrected for the low number of infarcts, κ ranged from 0.483 (insula) to 0.888 (M3), with greater agreement for cortical areas Intraclass correlation coefficients ranged from 0.09 (M1) to 0.556 (lentiform)
Patient Selection Alawieh et al., 201933 Lebanon, United States (multiple) Moderate Low Low Regression tree Sensitivity of 89.4% and specificity of 89.6% with AUC of 0.95
Negative predictive value was >95% Patients who were not selected by algorithm higher rates of symptomatic intracerebral hemorrhage after mechanical thrombectomy
Wang et al., 202069 United States (high) Moderate Low Low Convolutional neural network, random forest, support vector machine Voxel-wise AUC of 0.958, whereas other machine learning algorithms ranged from 0.897 to 0.933
Accuracy of 92%, with a sensitivity of 0.89 and specificity of 0.95, for retrospective determination for subject-level endovascular treatment eligibility
Voxel-wise AUC of 0.94 and a subject-level accuracy of 92% for endovascular treatment eligibility

NA, not applicable; CTA, computed tomography angiography; ASPECTS, Alberta Stroke Program Early CT Score; AUC, area under the curve.