Calibrated Residual Intelligence for Intra-Procedural CBCT–Based Collateral Grading in Ischemic Stroke

Brain stroke occurs when the brain’s blood supply is disrupted, leading to oxygen deprivation and rapid neuronal death. Ischemic stroke, the focus of this study, accounts for most cases and is strongly influenced by collateral circulation, a network of alternative vessels that stabilize perfusion when a primary artery is obstructed. Collateral status determines the extent of salvageable tissue and is typically graded manually using modalities such as magnetic resonance angiography (MRA), computed tomography (CT), and cone-beam computed tomography (CBCT), a process prone to subjectivity and inter-observer variability. This study proposes a ResNet-18–based deep learning framework for automated three-class classification of collateral circulation (Good, Moderate, Poor) from intra-procedural CBCT scans. A curated dataset of 45 patient cases (22,861 DICOM slices), annotated by an expert neuroradiologist, was preprocessed with patient-wise partitioning, normalization, and augmentation. The model achieved a validation accuracy of 88.8%, a micro-averaged precision–recall score of 0.947, and a macro-averaged ROC AUC of 0.958. Calibration analysis confirmed well-aligned probability estimates, while most misclassifications occurred in the Moderate class, reflecting inherent clinical ambiguity. Com-pared with prior CBCT studies using shallower architectures, the proposed framework demonstrates substantially higher accuracy, improved calibration, and enhanced robustness. These findings highlight the feasibility of ResNet-18 applied to CBCT imaging as a reliable and efficient tool to support neuroradiologists in collateral grading during hyperacute stroke management.