Artificial Intelligence-Assisted Detection of Intracranial Aneurysms on CTA and TOF-MRA: Small Lesions, Error Patterns, and Human–AI Collaboration

Abstract

Accurate detection of intracranial aneurysms is essential for further imaging assessment, specialist referral, and clinical management. Computed tomography angiography and time-of-flight magnetic resonance angiography are commonly used non-invasive methods for cerebrovascular examination. However, small aneurysms may go unnoticed due to their limited size, complex vascular anatomy, and imaging artifacts. Artificial intelligence (AI) can automatically identify suspicious lesions and may improve reader performance. This review summarizes studies published between 2022 and 2026 on AI-assisted detection of intracranial aneurysms using computed tomography angiography and time-of-flight magnetic resonance angiography. It focuses on the detection performance for small lesions, common false-positive and false-negative patterns, and the clinical value of human–AI collaboration. Current evidence suggests that artificial intelligence provides satisfactory overall detection performance. In some studies, it also improved clinicians' detection sensitivity and reading efficiency. However, aneurysms measuring ≤3 mm and some atypical lesions remain difficult to detect. Increasing model sensitivity may also result in more false-positive markings. Incorrect artificial intelligence suggestions may further lead to automation bias. At present, AI is more suitable as a lesion-alert tool and a second reader than as a replacement for clinicians in final diagnosis. Future research should investigate the causes of detection errors in relation to dataset composition, model design, external validation, and human–AI interaction. Diagnostic performance, false-positive burden, and effects on patient management should also be evaluated in real clinical settings.