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- Title
Machine Learning Application for Rupture Risk Assessment in Small-Sized Intracranial Aneurysm.
- Authors
Kim, Heung Cheol; Rhim, Jong Kook; Ahn, Jun Hyong; Park, Jeong Jin; Moon, Jong Un; Hong, Eun Pyo; Kim, Mi Ran; Kim, Seung Gyu; Lee, Seong Hwan; Jeong, Jae Hoon; Choi, Sung Won; Jeon, Jin Pyeong
- Abstract
The assessment of rupture probability is crucial to identifying at risk intracranial aneurysms (IA) in patients harboring multiple aneurysms. We aimed to develop a computer-assisted detection system for small-sized aneurysm ruptures using a convolutional neural network (CNN) based on images of three-dimensional digital subtraction angiography. A retrospective data set, including 368 patients, was used as a training cohort for the CNN using the TensorFlow platform. Aneurysm images in six directions were obtained from each patient and the region-of-interest in each image was extracted. The resulting CNN was prospectively tested in 272 patients and the sensitivity, specificity, overall accuracy, and receiver operating characteristics (ROC) were compared to a human evaluator. Our system showed a sensitivity of 78.76% (95% CI: 72.30%–84.30%), a specificity of 72.15% (95% CI: 60.93%–81.65%), and an overall diagnostic accuracy of 76.84% (95% CI: 71.36%–81.72%) in aneurysm rupture predictions. The area under the ROC (AUROC) in the CNN was 0.755 (95% CI: 0.699%–0.805%), better than that obtained from a human evaluator (AUROC: 0.537; p < 0.001). The CNN-based prediction system was feasible to assess rupture risk in small-sized aneurysms with diagnostic accuracy superior to human evaluators. Additional studies based on a large data set are necessary to enhance diagnostic accuracy and to facilitate clinical application.
- Subjects
INTRACRANIAL aneurysms; ARTIFICIAL neural networks; DIGITAL subtraction angiography; RISK assessment; MACHINE learning
- Publication
Journal of Clinical Medicine, 2019, Vol 8, Issue 5, p683
- ISSN
2077-0383
- Publication type
Article
- DOI
10.3390/jcm8050683