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Machine Learning Application for Rupture Risk Assessment in Small-Sized Intracranial Aneurysm

1
Department of Radiology, Hallym University College of Medicine, Chuncheon 24252, Korea
2
Department of Neurosurgery, Jeju National University College of Medicine, Jeju 63241, Korea
3
Department of Neurosurgery, Hallym University College of Medicine, Chuncheon 24252, Korea
4
Department of Neurology, Konkuk University Medical Center, Seoul 05030, Korea
5
Department of Neurosurgery, National Medical Center, Seoul 04564, Korea
6
Molecular Neurogenetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
7
Buzzpole Inc., Seoul 04781, Korea
8
Institute of New Frontier Stroke Research, Hallym University College of Medicine, Chuncheon 24252, Korea
9
Genetic and Research Inc., Chuncheon 24252, Korea
*
Author to whom correspondence should be addressed.
Contributed equally to this work.
J. Clin. Med. 2019, 8(5), 683; https://doi.org/10.3390/jcm8050683
Received: 22 April 2019 / Revised: 11 May 2019 / Accepted: 13 May 2019 / Published: 15 May 2019
(This article belongs to the Special Issue The Future of Artificial Intelligence in Clinical Medicine)
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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. View Full-Text
Keywords: intracranial aneurysm; convolutional neural network; subarachnoid hemorrhage intracranial aneurysm; convolutional neural network; subarachnoid hemorrhage
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MDPI and ACS Style

Kim, H.C.; Rhim, J.K.; Ahn, J.H.; Park, J.J.; Moon, J.U.; Hong, E.P.; Kim, M.R.; Kim, S.G.; Lee, S.H.; Jeong, J.H.; Choi, S.W.; Jeon, J.P. Machine Learning Application for Rupture Risk Assessment in Small-Sized Intracranial Aneurysm. J. Clin. Med. 2019, 8, 683.

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