Posterior circulation stroke: machine learning-based detection of early ischemic changes in acute non-contrast CT scans

Abstract

Objectives Triage of patients with basilar artery occlusion for additional imaging diagnostics, therapy planning, and initial outcome prediction requires assessment of early ischemic changes in early hyperacute non-contrast computed tomography (NCCT) scans. However, accuracy of visual evaluation is impaired by inter-and intra-reader variability, artifacts in the posterior fossa and limited sensitivity for subtle density shifts. We propose a machine learning approach for detecting early ischemic changes in pc-ASPECTS regions (Posterior circulation Alberta Stroke Program Early CT Score) based on admission NCCTs. Methods The retrospective study includes 552 pc-ASPECTS regions (144 with infarctions in follow-up NCCTs) extracted from pre-therapeutic early hyperacute scans of 69 patients with basilar artery occlusion that later underwent successful recanalization. We evaluated 1218 quantitative image features utilizing random forest algorithms with fivefold cross-validation for the ability to detect early ischemic changes in hyperacute images that lead to definitive infarctions in follow-up imaging. Classifier performance was compared to conventional readings of two neuroradiologists. Results Receiver operating characteristic area under the curves for detection of early ischemic changes were 0.70 (95% CI [0.64; 0.75]) for cerebellum to 0.82 (95% CI [0.77; 0.86]) for thalamus. Predictive performance of the classifier was significantly higher compared to visual reading for thalamus, midbrain, and pons (P value < 0.05). Conclusions Quantitative features of early hyperacute NCCTs can be used to detect early ischemic changes in pc-ASPECTS regions. The classifier performance was higher or equal to results of human raters. The proposed approach could facilitate reproducible analysis in research and may allow standardized assessments for outcome prediction and therapy planning in clinical routine.

Machine learning and acute stroke imaging

“…This algorithm was recently shown to correlate relatively well with human expert reads of Alberta Stroke Program Early CT (ASPECT) scores, and the best among three algorithms that were tested 23. Another recent publication demonstrated reasonable success for early ischemic changes in posterior circulation ASPECTS using NCHCT 24…”

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

“…76 Moreover, AI-based NCCT-ASPECTS was reported as good or better as human rating for posterior circulation stroke. 77…”

“…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. 78 Although AI-driven diagnostic processing is usually faster, it is not always superior to human rating, with AI showing less sensitivity in detecting LVO in CT angiography.…”

DGA3-Net: A parameter-efficient deep learning model for ASPECTS assessment for acute ischemic stroke using non-contrast computed tomography

“…In recent years, many automated AIS detection algorithms have been developed, and several commercial AI software platforms for automatic evaluation of the ASPECTS have been made available (see ( Murray et al, 2020 ) for a detailed review). Many methods incorporate handcrafted image features and traditional machine learning algorithms, such as support vector machines and random forest ( Aktar et al, 2020 , Kniep et al, 2020 , Kuang et al, 2019 , Bentley et al, 2014 , Takahashi et al, 2014 ). Notable examples include commercial software platforms for automated ASPECTS, including Brainomix e-ASPECTS and iSchemaView RAPID.…”