دورية أكاديمية
Augmenting Prediction of Intracranial Aneurysms' Risk Status Using Velocity-Informatics: Initial Experience
| العنوان: | Augmenting Prediction of Intracranial Aneurysms' Risk Status Using Velocity-Informatics: Initial Experience |
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| المؤلفون: | Jiang, J, Rezaeitaleshmahalleh, M, Lyu, Z, Mu, Nan, Ahmed, A S, Md, C M, Gemmete, J J, Pandey, A S |
| المصدر: | Michigan Tech Publications |
| بيانات النشر: | Digital Commons @ Michigan Tech |
| سنة النشر: | 2023 |
| المجموعة: | Michigan Technological University: Digital Commons @ Michigan Tech |
| مصطلحات موضوعية: | Hemodynamics, Intracranial Aneurysm, Machine Learning, Rupture Risk Prediction, Velocity-informatics |
| الوصف: | Our primary goal here is to demonstrate that innovative analytics of aneurismal velocities, named velocity-informatics, enhances intracranial aneurysm (IA) rupture status prediction. 3D computer models were generated using imaging data from 112 subjects harboring anterior IAs (4-25 mm; 44 ruptured and 68 unruptured). Computational fluid dynamics simulations and geometrical analyses were performed. Then, computed 3D velocity vector fields within the IA dome were processed for velocity-informatics. Four machine learning methods (support vector machine, random forest, generalized linear model, and GLM with Lasso or elastic net regularization) were employed to assess the merits of the proposed velocity-informatics. All 4 ML methods consistently showed that, with velocity-informatics metrics, the area under the curve and prediction accuracy both improved by approximately 0.03. Overall, with velocity-informatics, the support vector machine's prediction was most promising: an AUC of 0.86 and total accuracy of 77%, with 60% and 88% of ruptured and unruptured IAs being correctly identified, respectively. |
| نوع الوثيقة: | text |
| اللغة: | unknown |
| العلاقة: | https://digitalcommons.mtu.edu/michigantech-p/17181Test; https://doi.org/10.1007/s12265-023-10394-6Test |
| DOI: | 10.1007/s12265-023-10394-6 |
| الإتاحة: | https://doi.org/10.1007/s12265-023-10394-6Test https://digitalcommons.mtu.edu/michigantech-p/17181Test |
| رقم الانضمام: | edsbas.DB27D4D9 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1007/s12265-023-10394-6 |
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