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Machine Learning-Enabled Detection of Unruptured Cerebral Aneurysms Improves Detection Rates and Clinical Care.
Hyun-Woo Kim1, Anjan Ballekere1, Iman Ali1
1Department of Neurology UTHealth McGovern Medical School Houston TX.
Stroke (Hoboken, N.J.)
|January 26, 2026
Summary
A machine learning algorithm can identify unruptured cerebral aneurysms (UCAs) missed in routine care. This AI tool shows promise for improving UCA detection and patient management, potentially preventing subarachnoid hemorrhage.
Area of Science:
- Neurology
- Radiology
- Artificial Intelligence
Background:
- Unruptured cerebral aneurysms (UCAs) affect approximately 3% of the population.
- Early detection of UCAs is crucial for preventing subarachnoid hemorrhage.
- Current clinical practices may miss UCAs requiring attention.
Purpose of the Study:
- To evaluate the performance of a machine learning algorithm in identifying UCAs.
- To determine if routine use of this algorithm enhances UCA detection and patient care.
Main Methods:
- A convolutional deep neural network (Viz ANEURYSM) was trained to detect UCAs ≥4 mm.
- 1191 computed tomography angiograms were analyzed from a multicenter registry (March-November 2021).
- Ground truth was established by blinded expert neuroradiologist review.
Main Results:
- The algorithm flagged 50 (4.2%) studies, with 31 true positives (62% PPV).
- 10 (27.8%) confirmed UCAs were missed in clinical reports.
- 24 (67%) detected UCAs were not referred for follow-up, including large ones (>7 mm).
Conclusions:
- UCAs requiring intervention are often overlooked in standard clinical workflows.
- A machine learning algorithm can flag missed UCAs, improving detection rates.
- Implementing such AI tools may reduce missed care opportunities for UCAs.
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