Artificial intelligence-driven ASPECTS for the detection of early stroke changes in non-contrast CT: a systematic review and meta-analysis

Abstract

BackgroundRecent 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.MethodsThe 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).ResultsEleven 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).ConclusionsArtificial intelligence-driven ASPECTS software has comparable or better performance than physicians in terms of recognizing early stroke changes on NCCT.

Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT

“…A recent meta-analysis compared the performance of automated and manual ASPECTS predictions for early stroke changes. It reported good reliability between reference standards and both expert (ICC 0.62 [95% CI 0.52 to 0.71]) and automated predictions (ICC 0.72 [95% CI 0.61 to 0.80]), while concluding that automated prediction may be superior, based on higher ICC values 23. The ICC in our study was 0.78 [95% CI: 0.73 to 0.82] showing good-to-excellent agreement, with results comparable to the meta-analysis 23.…”

“…It reported good reliability between reference standards and both expert (ICC 0.62 [95% CI 0.52 to 0.71]) and automated predictions (ICC 0.72 [95% CI 0.61 to 0.80]), while concluding that automated prediction may be superior, based on higher ICC values 23. The ICC in our study was 0.78 [95% CI: 0.73 to 0.82] showing good-to-excellent agreement, with results comparable to the meta-analysis 23. In clinical situations, however, ASPECTS is often used in a dichotomized fashion, for selection of patients for reperfusion treatments.…”

Priorities for Advancements in Neuroimaging in the Diagnostic Workup of Acute Stroke

“…An AI model outperformed expert readers in detecting early ischemic changes on NCCT in a recent study, 75 and a systematic review including 11 studies and 1976 cases revealed that AI-based ASPECTS performed similar or better than radiologists in identifying early stroke changes on NCCT. 76 Moreover, AI-based NCCT-ASPECTS was reported as good or better as human rating for posterior circulation stroke. 77 However, the accuracy and reliability of AI- and human-based NCCT-ASPECTS depends on time from stroke onset to imaging and is lower in hyperacute stroke and fast stroke progressors.…”

Most Promising Approaches to Improve Stroke Outcomes: The Stroke Treatment Academic Industry Roundtable XII Workshop

“…Emerging AI technologies may offer more rapid identification of these patients. For instance, AI approaches to detect stroke and the lesion core from noncontrast computed tomography scans are currently being investigated 69 and may accelerate the identification of patients eligible for recanalization therapies. AI stroke diagnosis could be applied in primary and secondary centers and may work autonomously.…”