Diagnostic Accuracy of a Deep Learning Algorithm for Detecting Unruptured Intracranial Aneurysms in Magnetic Resonance Angiography: A Multicenter Pivotal Trial

BACKGROUND: Intracranial aneurysm rupture is associated with high mortality and disability rates. Early detection is crucial, but increasing diagnostic workloads place significant strain on radiologists. We evaluated the efficacy of a deep learning algorithm in detecting unruptured intracranial aneurysms (UIAs) using time-of-flight (TOF) magnetic resonance angiography (MRA). METHODS: Data from 675 participants (189 aneurysm-positive [221 UIAs] and 486 aneurysm-negative) were collected from 2 hospitals (2019-2023). Positive cases were confirmed by digital subtraction angiography, and images were annotated by vascular experts. The 3D U-Net-based model was trained on 988 nonoverlapped TOF MRA datasets and evaluated by patient- and lesion-level sensitivity, specificity, and false-positive rates. RESULTS: The mean age was 59.6 years (standard deviation 11.3), and 52.0% were female. The model achieved patient-level sensitivity of 95.2% and specificity of 80.5%, with lesion-level sensitivity of 89.6% and a false-positive rate of 0.19 per patient. Sensitivity by aneurysm size was 72.3% for lesions <3 mm, 91.8% for 3-5 mm, and 94.3% for >5 mm. Performance was consistent across institutions, with an area under the receiver operating characteristic curve of 0.949. CONCLUSIONS: The software demonstrated high sensitivity and low false-positive rates for UIA detection in TOF MRA, suggesting its utility in reducing diagnostic errors and alleviating radiologist workload. Expert review remains essential, particularly for small or complex aneurysms.