Methodological Challenges in Deep Learning-Based Detec-tion of Intracranial Aneurysms: A Scoping Review

Abstract

Artificial intelligence (AI), particularly deep learning, has demonstrated high diagnostic performance in detecting intracranial aneurysms on computed tomography angiography (CTA) and magnetic resonance angiography (MRA). However, the clinical translation of these technologies remains limited due to methodological limitations and concerns about generalizability. This scoping review comprehensively evaluates 36 studies that applied deep learning to intracranial aneurysm detection on CTA or MRA, focusing on study design, validation strategies, reporting practices, and reference standards. Key findings include inconsistent handling of ruptured and previously treated aneurysms, underreporting of coexisting brain or vascular abnormalities, limited use of external validation, and an almost complete absence of prospective study designs. Only a minority of studies employed diagnostic cohorts that reflect real-world aneurysm prevalence, and few reported all essential performance metrics, such as patient-wise and lesion-wise sensitivity, specificity, and false positives per case. These limitations suggest that current studies remain at the stage of technical validation, with high risks of bias and limited clinical applicability. To facilitate real-world implementation, future research must adopt more rigorous designs, representative and diverse validation cohorts, standardized reporting practices, and greater attention to human-AI interaction.