Validation of an Automated ASPECTS Software via a Multi-Reader Multi-Case Clinical Reader Study

BACKGROUND: Rapid, accurate detection of early ischemic changes (EIC) on non-contrast computed tomography (NCCT) is critical for the triage and treatment of acute ischemic stroke (AIS) patients, yet NCCT interpretation remains challenging due to low soft-tissue contrast and inter-reader variability. MATERIALS AND METHODS: This study validates StrokeSENS ASPECTS, a fully automated AI-based software tool designed to aid clinicians in grading region-level EIC. A fully crossed multi-reader multi-case (MRMC) clinical reader study was conducted with eight clinicians who independently scored 100 NCCT scans from patients with confirmed middle cerebral artery (MCA)/internal carotid artery (ICA) occlusion, unaided and aided by StrokeSENS ASPECTS, with a three-expert-neuroradiologist consensus serving as the reference standard. Reader performance was assessed using binary classification metrics, including balanced accuracy, overall accuracy, sensitivity, and specificity; inter-reader agreement was assessed using Fleiss's kappa. RESULTS: 0.001). Inter-reader agreement showed a significant increase in Fleiss's Kappa of 0.285 from 0.323% (unaided) to 0.608% (aided). CONCLUSION: StrokeSENS ASPECTS have shown to improve clinicians' ability to detect EIC on NCCT and reduce inter-reader variability. This demonstrates StrokeSENS ASPECTS's safety and effectiveness as an aid in the evaluation of AIS.