Diagnostic Accuracy of a Deep Learning Algorithm for Detecting Unruptured Intracranial Aneurysms in Magnetic Resonance Angiography: A Multicenter Pivotal Trial
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.
Authors
- Dougho Park (ORCID: https://orcid.org/0000-0002-1288-470X)
- Jaechan Park (ORCID: https://orcid.org/0000-0001-7572-3260)
- Wi‐Sun Ryu (ORCID: https://orcid.org/0000-0002-2823-5253)
- Myung-Jae Lee (ORCID: https://orcid.org/0000-0003-0475-1437)
- Hui Joong Lee (ORCID: https://orcid.org/0000-0002-1279-3795)
- Byoung-Joon Kim
- Sungmoon Jeong (ORCID: https://orcid.org/0000-0002-4579-3150)
- Dongmin Kim (ORCID: https://orcid.org/0000-0002-3033-6010)
- Heeyoung Kim (ORCID: https://orcid.org/0000-0002-3637-5977)
- Myungsoo Kim (ORCID: https://orcid.org/0000-0003-2764-2508)
Institutions
- Pohang University of Science and Technology (KR)
- Kwangwoon University (KR)
- Kyungpook National University Hospital (KR)
- Kyungpook National University (KR)
- Pohang TechnoPark (South Korea) (KR)
Publication Details
- Journal
- World Neurosurgery
- Published
- 2025-03-12
- DOI
- https://doi.org/10.1016/j.wneu.2025.123882
- Citations
- 2
- Primary Topic
- Intracranial Aneurysms: Treatment and Complications
- Type
- article
- Field-Weighted Citation Impact
- 2.78