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

Abstract

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.