182 related articles for article (PubMed ID: 33664115)
1. Machine Learning-Based Prediction of Small Intracranial Aneurysm Rupture Status Using CTA-Derived Hemodynamics: A Multicenter Study.
Shi Z; Chen GZ; Mao L; Li XL; Zhou CS; Xia S; Zhang YX; Zhang B; Hu B; Lu GM; Zhang LJ
AJNR Am J Neuroradiol; 2021 Apr; 42(4):648-654. PubMed ID: 33664115
[TBL] [Abstract][Full Text] [Related]
2. Development and validation of machine learning prediction model based on computed tomography angiography-derived hemodynamics for rupture status of intracranial aneurysms: a Chinese multicenter study.
Chen G; Lu M; Shi Z; Xia S; Ren Y; Liu Z; Liu X; Li Z; Mao L; Li XL; Zhang B; Zhang LJ; Lu GM
Eur Radiol; 2020 Sep; 30(9):5170-5182. PubMed ID: 32350658
[TBL] [Abstract][Full Text] [Related]
3. Identification of Small, Regularly Shaped Cerebral Aneurysms Prone to Rupture.
Salimi Ashkezari SF; Mut F; Slawski M; Jimenez CM; Robertson AM; Cebral JR
AJNR Am J Neuroradiol; 2022 Apr; 43(4):547-553. PubMed ID: 35332023
[TBL] [Abstract][Full Text] [Related]
4. Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.
Paliwal N; Jaiswal P; Tutino VM; Shallwani H; Davies JM; Siddiqui AH; Rai R; Meng H
Neurosurg Focus; 2018 Nov; 45(5):E7. PubMed ID: 30453461
[TBL] [Abstract][Full Text] [Related]
5. Prediction of bleb formation in intracranial aneurysms using machine learning models based on aneurysm hemodynamics, geometry, location, and patient population.
Salimi Ashkezari SF; Mut F; Slawski M; Cheng B; Yu AK; White TG; Woo HH; Koch MJ; Amin-Hanjani S; Charbel FT; Rezai Jahromi B; Niemelä M; Koivisto T; Frosen J; Tobe Y; Maiti S; Robertson AM; Cebral JR
J Neurointerv Surg; 2022 Oct; 14(10):1002-1007. PubMed ID: 34686573
[TBL] [Abstract][Full Text] [Related]
6. Development and validation of a deep learning model for prediction of intracranial aneurysm rupture risk based on multi-omics factor.
Turhon M; Li M; Kang H; Huang J; Zhang F; Zhang Y; Zhang Y; Maimaiti A; Gheyret D; Axier A; Aisha M; Yang X; Liu J
Eur Radiol; 2023 Oct; 33(10):6759-6770. PubMed ID: 37099175
[TBL] [Abstract][Full Text] [Related]
7. Discrimination of intracranial aneurysm rupture status: patient-specific inflow boundary may not be a must-have condition in hemodynamic simulations.
Li W; Wang S; Tian Z; Zhu W; Zhang Y; Zhang Y; Wang Y; Wang K; Yang X; Liu J
Neuroradiology; 2020 Nov; 62(11):1485-1495. PubMed ID: 32588092
[TBL] [Abstract][Full Text] [Related]
8. Assessing the Risk of Intracranial Aneurysm Rupture Using Morphological and Hemodynamic Biomarkers Evaluated from Magnetic Resonance Fluid Dynamics and Computational Fluid Dynamics.
Perera R; Isoda H; Ishiguro K; Mizuno T; Takehara Y; Terada M; Tanoi C; Naito T; Sakahara H; Hiramatsu H; Namba H; Izumi T; Wakabayashi T; Kosugi T; Onishi Y; Alley M; Komori Y; Ikeda M; Naganawa S
Magn Reson Med Sci; 2020 Dec; 19(4):333-344. PubMed ID: 31956175
[TBL] [Abstract][Full Text] [Related]
9. Morphological-Hemodynamic Characteristics of Intracranial Bifurcation Mirror Aneurysms.
Fan J; Wang Y; Liu J; Jing L; Wang C; Li C; Yang X; Zhang Y
World Neurosurg; 2015 Jul; 84(1):114-120.e2. PubMed ID: 25753233
[TBL] [Abstract][Full Text] [Related]
10. Shared and Distinct Rupture Discriminants of Small and Large Intracranial Aneurysms.
Varble N; Tutino VM; Yu J; Sonig A; Siddiqui AH; Davies JM; Meng H
Stroke; 2018 Apr; 49(4):856-864. PubMed ID: 29535267
[TBL] [Abstract][Full Text] [Related]
11. Local hemodynamics at the rupture point of cerebral aneurysms determined by computational fluid dynamics analysis.
Omodaka S; Sugiyama S; Inoue T; Funamoto K; Fujimura M; Shimizu H; Hayase T; Takahashi A; Tominaga T
Cerebrovasc Dis; 2012; 34(2):121-9. PubMed ID: 22965244
[TBL] [Abstract][Full Text] [Related]
12. [CT-based morphological and hemodynamics analysis for rupture risk of mirror intracranial aneurysm].
Hu B; Li DC; Xu WD; Shi Z; Zhang LJ
Zhonghua Yi Xue Za Zhi; 2022 Feb; 102(5):350-356. PubMed ID: 35092976
[No Abstract] [Full Text] [Related]
13. Impact of bifurcation angle and inflow coefficient on the rupture risk of bifurcation type basilar artery tip aneurysms.
Rashad S; Sugiyama SI; Niizuma K; Sato K; Endo H; Omodaka S; Matsumoto Y; Fujimura M; Tominaga T
J Neurosurg; 2018 Mar; 128(3):723-730. PubMed ID: 28298037
[TBL] [Abstract][Full Text] [Related]
14. Cerebral aneurysm rupture status classification using statistical and machine learning methods.
Amigo N; Valencia A; Wu W; Patnaik S; Finol E
Proc Inst Mech Eng H; 2021 Jun; 235(6):655-662. PubMed ID: 33685288
[TBL] [Abstract][Full Text] [Related]
15. Comparison of statistical learning approaches for cerebral aneurysm rupture assessment.
Detmer FJ; Lückehe D; Mut F; Slawski M; Hirsch S; Bijlenga P; von Voigt G; Cebral JR
Int J Comput Assist Radiol Surg; 2020 Jan; 15(1):141-150. PubMed ID: 31485987
[TBL] [Abstract][Full Text] [Related]
16. Hemodynamic-morphologic discriminants for intracranial aneurysm rupture.
Xiang J; Natarajan SK; Tremmel M; Ma D; Mocco J; Hopkins LN; Siddiqui AH; Levy EI; Meng H
Stroke; 2011 Jan; 42(1):144-52. PubMed ID: 21106956
[TBL] [Abstract][Full Text] [Related]
17. A pilot study using a machine-learning approach of morphological and hemodynamic parameters for predicting aneurysms enhancement.
Lv N; Karmonik C; Shi Z; Chen S; Wang X; Liu J; Huang Q
Int J Comput Assist Radiol Surg; 2020 Aug; 15(8):1313-1321. PubMed ID: 32514728
[TBL] [Abstract][Full Text] [Related]
18. Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture.
Silva MA; Patel J; Kavouridis V; Gallerani T; Beers A; Chang K; Hoebel KV; Brown J; See AP; Gormley WB; Aziz-Sultan MA; Kalpathy-Cramer J; Arnaout O; Patel NJ
World Neurosurg; 2019 Nov; 131():e46-e51. PubMed ID: 31295616
[TBL] [Abstract][Full Text] [Related]
19. [Hemodynamics-based analysis of factors associated with aneurysm rupture in different sides of the internal carotid artery].
Xu WD; Shi Z; Hu B; Zhang LJ; Lu GM
Zhonghua Yi Xue Za Zhi; 2021 Jun; 101(23):1798-1804. PubMed ID: 34167280
[No Abstract] [Full Text] [Related]
20. Extending statistical learning for aneurysm rupture assessment to Finnish and Japanese populations using morphology, hemodynamics, and patient characteristics.
Detmer FJ; Hadad S; Chung BJ; Mut F; Slawski M; Juchler N; Kurtcuoglu V; Hirsch S; Bijlenga P; Uchiyama Y; Fujimura S; Yamamoto M; Murayama Y; Takao H; Koivisto T; Frösen J; Cebral JR
Neurosurg Focus; 2019 Jul; 47(1):E16. PubMed ID: 31261120
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]