ArticlePeerJ2026
AI-based detection and sizing of saccular intracranial aneurysms: a single-center retrospective validation study using computed tomography angiography.
Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Imaging advantages have raised intracranial aneurysm (IA) detection rates but have also increased radiologists' workloads. Coupled with visual fatigue, this heavier burden heightens the risk of missed or erroneous diagnoses. Concurrently, artificial intelligence (AI) has shown great promise for analyzing medical images. This study aims to evaluate the diagnostic performance of AI software for IAs and to provide an initial, single-centre validation of its potential as a supportive tool in future deployment. Methods: Between January 2019 and September 2023, 452 patients with 544 IAs diagnosed by head and neck computed tomography angiography (CTA) who also underwent digital subtraction angiography (DSA) were included. The AI's ability to detect the presence, location, and size of IAs was recorded. Its results were compared with DSA, and the agreement between AI and radiologists in measuring IA size was evaluated. Results: The AI software demonstrated a sensitivity of 88.97% (95% CI [0.861-0.963]) and an accuracy of 75.04% (95% CI [0.715-0.783]) for detecting IAs. Specifically, the accuracy and sensitivity of AI in detecting IAs that are smaller than three mm, between 3-5 mm, and larger than five mm are 58.46% (95% CI [0.462-0.698]) and 66.67% (95% CI [0.537-0.775]), 76.68% (95% CI [0.714-0.813]) and 88.93% (95% CI [0.843-0.924]), 77.10% (95% CI [0.720-0.816]) and 94.24% (95% CI [0.906-0.966]), respectively. There was good agreement between radiologists and DSA, between AI and DSA, and between radiologists and AI for identifying the location of IAs, with kappa values all greater than 0.75. The radiologists and AI also showed good consistency in measuring depth, width, height, and maximum diameter, with intraclass correlation coefficients (ICCs) all greater than 0.75, except for the neck width, which had an ICC of 0.492. Conclusions: The AI software performs well in terms of detecting IAs that are larger than three mm and shows good agreement with radiologists in localizing positions and extracting their morphometric parameters (except for neck width measurements). The AI software has proved to be a reliable adjunct for IA detection and measurement tasks.
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