Diagnostic Accuracy and Time Efficiency of Artificial Intelligence for Intracranial Hemorrhage Detection on Emergency Brain CT

Background: Intracranial hemorrhage (ICH) is a neurological emergency associated with high mortality and morbidity, requiring rapid diagnosis and early intervention. Artificial intelligence (AI)-based imaging systems have recently gained attention as supportive tools for accelerating emergency brain computed tomography (CT) interpretation. This study aimed to evaluate the diagnostic accuracy and time efficiency of an AI system for detecting ICH on emergency brain CT scans. Methods: This retrospective study included 375 patients who underwent non-contrast brain CT in the emergency department. AI-based image analysis results were compared with final radiologist reports accepted as the reference standard. Sensitivity, specificity, predictive values, accuracy, area under the curve (AUC), and Cohen’s kappa were calculated. Interpretation times, subgroup analyses according to presentation type and work shifts, and hemorrhage volume correlations between AI and the ABC/2 method were also evaluated. Results: Intracranial hemorrhage was detected in 235 patients (62.7%). The AI system achieved a sensitivity of 94.0%, specificity of 87.9%, positive predictive value of 92.9%, negative predictive value of 89.8%, and overall accuracy of 91.7%. The AUC was 0.909, with a Cohen’s kappa coefficient of 0.823. Mean interpretation time was significantly shorter for AI compared with radiologists (11.9 ± 3.0 vs. 70.9 ± 25.4 min, p < 0.001). AI-derived hemorrhage volume measurements strongly correlated with the ABC/2 method (rho = 0.887, p < 0.001). Conclusions: The AI system demonstrated high diagnostic accuracy and a significant reduction in interpretation time for detecting intracranial hemorrhage on emergency brain CT, supporting its potential role as a decision-support tool in emergency radiology workflows.