BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

185 related articles for article (PubMed ID: 37760168)

  • 1. The Potential of Deep Learning to Advance Clinical Applications of Computational Biomechanics.
    Truskey GA
    Bioengineering (Basel); 2023 Sep; 10(9):. PubMed ID: 37760168
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Synergistic Integration of Deep Neural Networks and Finite Element Method with Applications of Nonlinear Large Deformation Biomechanics.
    Liang L; Liu M; Elefteriades J; Sun W
    Comput Methods Appl Mech Eng; 2023 Nov; 416():. PubMed ID: 38370344
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Robust automatic hexahedral cartilage meshing framework enables population-based computational studies of the knee.
    Gibbons KD; Malbouby V; Alvarez O; Fitzpatrick CK
    Front Bioeng Biotechnol; 2022; 10():1059003. PubMed ID: 36568304
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Semi-supervised learning for automatic segmentation of the knee from MRI with convolutional neural networks.
    Burton W; Myers C; Rullkoetter P
    Comput Methods Programs Biomed; 2020 Jun; 189():105328. PubMed ID: 31958580
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A Predictive Analysis of Wall Stress in Abdominal Aortic Aneurysms Using a Neural Network Model.
    Rengarajan B; Patnaik SS; Finol EA
    J Biomech Eng; 2021 Dec; 143(12):. PubMed ID: 34318314
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Finite element methods for the biomechanics of soft hydrated tissues: nonlinear analysis and adaptive control of meshes.
    Spilker RL; de Almeida ES; Donzelli PS
    Crit Rev Biomed Eng; 1992; 20(3-4):279-313. PubMed ID: 1478094
    [TBL] [Abstract][Full Text] [Related]  

  • 7. From Finite Element Meshes to Clouds of Points: A Review of Methods for Generation of Computational Biomechanics Models for Patient-Specific Applications.
    Wittek A; Grosland NM; Joldes GR; Magnotta V; Miller K
    Ann Biomed Eng; 2016 Jan; 44(1):3-15. PubMed ID: 26424475
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond.
    Arzani A; Wang JX; Sacks MS; Shadden SC
    Ann Biomed Eng; 2022 Jun; 50(6):615-627. PubMed ID: 35445297
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Computational Models of the Foot and Ankle for Pathomechanics and Clinical Applications: A Review.
    Wang Y; Wong DW; Zhang M
    Ann Biomed Eng; 2016 Jan; 44(1):213-21. PubMed ID: 26101032
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning.
    Lampen N; Kim D; Fang X; Xu X; Kuang T; Deng HH; Barber JC; Gateno J; Xia J; Yan P
    Int J Comput Assist Radiol Surg; 2022 May; 17(5):945-952. PubMed ID: 35362849
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A method for incorporating three-dimensional residual stretches/stresses into patient-specific finite element simulations of arteries.
    Pierce DM; Fastl TE; Rodriguez-Vila B; Verbrugghe P; Fourneau I; Maleux G; Herijgers P; Gomez EJ; Holzapfel GA
    J Mech Behav Biomed Mater; 2015 Jul; 47():147-164. PubMed ID: 25931035
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Image-based biomechanical models of the musculoskeletal system.
    Galbusera F; Cina A; Panico M; Albano D; Messina C
    Eur Radiol Exp; 2020 Aug; 4(1):49. PubMed ID: 32789547
    [TBL] [Abstract][Full Text] [Related]  

  • 13. An automated computational biomechanics workflow for improving breast cancer diagnosis and treatment.
    Babarenda Gamage TP; Malcolm DTK; Maso Talou G; Mîra A; Doyle A; Nielsen PMF; Nash MP
    Interface Focus; 2019 Aug; 9(4):20190034. PubMed ID: 31263540
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery.
    Tonutti M; Gras G; Yang GZ
    Artif Intell Med; 2017 Jul; 80():39-47. PubMed ID: 28750949
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Deep Learning Approaches to Surrogates for Solving the Diffusion Equation for Mechanistic Real-World Simulations.
    Toledo-Marín JQ; Fox G; Sluka JP; Glazier JA
    Front Physiol; 2021; 12():667828. PubMed ID: 34248661
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Data-Driven Computational Simulation in Bone Mechanics.
    Sanz-Herrera JA; Mora-Macías J; Ayensa-Jiménez J; Reina-Romo E; Doweidar MH; Domínguez J; Doblaré M
    Ann Biomed Eng; 2021 Jan; 49(1):407-419. PubMed ID: 32681405
    [TBL] [Abstract][Full Text] [Related]  

  • 17. FDM data driven U-Net as a 2D Laplace PINN solver.
    Maria Antony AN; Narisetti N; Gladilin E
    Sci Rep; 2023 Jun; 13(1):9116. PubMed ID: 37277366
    [TBL] [Abstract][Full Text] [Related]  

  • 18. On methodology and application of smoothed particle hydrodynamics in fluid, solid and biomechanics.
    Xu F; Wang J; Yang Y; Wang L; Dai Z; Han R
    Acta Mech Sin; 2023; 39(2):722185. PubMed ID: 36776492
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep learning-based automatic segmentation of images in cardiac radiography: A promising challenge.
    Song Y; Ren S; Lu Y; Fu X; Wong KKL
    Comput Methods Programs Biomed; 2022 Jun; 220():106821. PubMed ID: 35487181
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Simulation of hyperelastic materials in real-time using deep learning.
    Mendizabal A; Márquez-Neila P; Cotin S
    Med Image Anal; 2020 Jan; 59():101569. PubMed ID: 31704451
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.