ArticleMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine2026
Evaluation of Approved AI-based Brain Aneurysm Detection Software in Clinical Practice: Comparison with Radiologist Assessment and Image Re-review.
Article in Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Recent advances in MR neuroimaging: toward quantitative and AI-driven brain and spinal cord imaging.Japanese journal of radiology · 2026Review
- Clinical artificial intelligence (AI) in Japan: Regulatory pathways, domain-specific evidence, and its data infrastructure from an international perspective.Global health & medicine · 2026Review
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6 authors.
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Abstract
purposeThis study evaluated the performance of artificial intelligence (AI)-based brain aneurysm detection software in clinical settings, aiming to assess its utility as a supportive tool for radiologists. Metrics included sensitivity, positive predictive value (PPV), F1 score, and false positives (FPs) per case.
methodsA retrospective analysis of 442 cases (March 2023-August 2024) compared AI detections against a reference standard derived from the radiologists' assessments and image re-review. Findings were categorized into true positives (TPs), FPs, and false negatives (FNs). Subgroup analyses covered aneurysm size, magnetic field strength of the MRI, patient age, and aneurysm location.
resultsThe study included 442 cases (226 males, 216 females; median age 72). Out of 94 total aneurysms, the AI detected 73 TP and missed 21 FN. It also identified 520 FP. Overall, sensitivity was 77.7%, PPV was 12.3%, and the F1 score was 0.212. The FPs averaged 1.18 per case. Sensitivity varied by aneurysm size: 85.1% for ≤ 3 mm, 69.2% for 3-5 mm, and 50.0% for > 5 mm. Significant variability in FPs per case was observed across different magnetic field strengths. Performance also varied by patient age and aneurysm location.
conclusionThe AI software demonstrated moderate sensitivity, especially for smaller aneurysms. Variations in performance across different magnetic field strengths and aneurysm size suggest a need for more robust AI algorithms. Detailed analysis of aneurysm locations provides insights into areas where AI performance could be enhanced. Integrating the AI software as a supportive tool, combined with radiologist expertise, is hypothesized to enhance detection accuracy, though further studies are needed to quantify this combined effect.
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