A Dual Machine Learning Framework for Automated ASPECTS Scoring and Treatment Eligibility in Acute Ischemic Stroke

The Alberta Stroke Program Early CT Score (ASPECTS) is a standardized method to assess early ischemic changes in the middle cerebral artery (MCA) territory on Non-contrast computed tomography (NCCT) scans. It plays a critical role in acute stroke management by guiding clinicians identify candidates for intravenous thrombolysis or mechanical thrombectomy based on the extent of ischemic damage. However, manual ASPECTS evaluation suffers from inter-observer variability and depends heavily on clinician experience. This study proposes a machine learning-based approach using two complementary models : the first classifies patients according to their eligibility for revascularization treatments, while the second identifies affected and unaffected ASPECTS regions and automatically estimates the ASPECTS score. Both models were trained and evaluated on a dataset of 40 patients, with annotated CT slices from the publicly available AISD dataset (Acute Ischemic Stroke Dataset, N=397). The approach achieved promising performance, with 93,33% accuracy in patient classification, and 93% accuracy for detecting affected and unaffected regions of interest (ROIs). However, the small sample size (40 patients) and lack of external validation limit the generalizability of the findings. Future work will focus on automating region of interest segmentation using deep learning and improving the models’ generalizability across diverse datasets and clinical environments.