Machine Learning Application for Rupture Risk Assessment in Small-Sized Intracranial Aneurysm
AbstractThe 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
Share & Cite This Article
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.
Kim HC, Rhim JK, Ahn JH, Park JJ, Moon JU, Hong EP, Kim MR, Kim SG, Lee SH, Jeong JH, Choi SW, Jeon JP. Machine Learning Application for Rupture Risk Assessment in Small-Sized Intracranial Aneurysm. Journal of Clinical Medicine. 2019; 8(5):683.Chicago/Turabian Style
Kim, Heung C.; Rhim, Jong K.; Ahn, Jun H.; Park, Jeong J.; Moon, Jong U.; Hong, Eun P.; Kim, Mi R.; Kim, Seung G.; Lee, Seong H.; Jeong, Jae H.; Choi, Sung W.; Jeon, Jin P. 2019. "Machine Learning Application for Rupture Risk Assessment in Small-Sized Intracranial Aneurysm." J. Clin. Med. 8, no. 5: 683.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.