Cargando…

The Potential of Deep Learning to Advance Clinical Applications of Computational Biomechanics

When combined with patient information provided by advanced imaging techniques, computational biomechanics can provide detailed patient-specific information about stresses and strains acting on tissues that can be useful in diagnosing and assessing treatments for diseases and injuries. This approach...

Descripción completa

Detalles Bibliográficos
Autor principal:	Truskey, George A.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2023
Materias:	Review
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10525821/ https://www.ncbi.nlm.nih.gov/pubmed/37760168 http://dx.doi.org/10.3390/bioengineering10091066

Ejemplares similares

Advancing cardiovascular tissue engineering
por: Truskey, George A.
Publicado: (2016)

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications
por: Wu, Yawen, et al.
Publicado: (2022)

Editorial: Computational Biomechanics of the Heart and Vasculature With Potential Clinical and Surgical Applications
por: Li, Zhiyong, et al.
Publicado: (2022)

Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications
por: Andreini, Claudia, et al.
Publicado: (2022)

Machine learning paradigms: advances in deep learning-based technological applications
por: Tsihrintzis, George, et al.
Publicado: (2020)

Advances in deep learning for computed tomography denoising
por: Park, Sung Bin
Publicado: (2021)

Guest Editorial: Advances in Deep Learning for Clinical and Healthcare Applications
por: Ieracitano, Cosimo, et al.
Publicado: (2022)

Deep learning for computational biology
por: Angermueller, Christof, et al.
Publicado: (2016)

Biomechanical Computed Tomography analysis (BCT) for clinical assessment of osteoporosis
por: Keaveny, T.M., et al.
Publicado: (2020)

Advanced 4-dimensional cone-beam computed tomography reconstruction by combining motion estimation, motion-compensated reconstruction, biomechanical modeling and deep learning
por: Zhang, You, et al.
Publicado: (2019)

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets
por: Bayoudh, Khaled, et al.
Publicado: (2021)

Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics
por: Class, Lisa-Carina, et al.
Publicado: (2021)

Advances in Computational Techniques for Bio-Inspired Cellular Materials in the Field of Biomechanics: Current Trends and Prospects
por: Pais, A. I., et al.
Publicado: (2023)

Computational biology: deep learning
por: Jones, William, et al.
Publicado: (2017)

Recent Advances of Deep Learning in Bioinformatics and Computational Biology
por: Tang, Binhua, et al.
Publicado: (2019)

Advances in Human Placental Biomechanics
por: Plitman Mayo, R.
Publicado: (2018)

Deep metabolome: Applications of deep learning in metabolomics
por: Pomyen, Yotsawat, et al.
Publicado: (2020)

G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learning
por: Zhang, Enrui, et al.
Publicado: (2022)

Computational methods for deep learning: theoretic, practice and applications
por: Yan, Wei Qi
Publicado: (2021)

Computational biomechanics for medicine: from algorithms to models and applications
por: Wittek, Adam, et al.
Publicado: (2017)

Computational biomechanics for medicine: new approaches and new applications
por: Doyle, Barry, et al.
Publicado: (2015)

Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges
por: Hwang, Eui Jin, et al.
Publicado: (2020)

Advances in endodontics: Potential applications in clinical practice
por: Kishen, Anil, et al.
Publicado: (2016)

Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects
por: Chen, Chunlei, et al.
Publicado: (2023)

Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review
por: Li, Rui, et al.
Publicado: (2022)

Advances in computational intelligence and learning: methods and applications
por: Zimmermann, Hans-Jürgen, et al.
Publicado: (2002)

Recent advances of deep learning in psychiatric disorders
por: Chen, Lu, et al.
Publicado: (2020)

Biomechanical Analysis of Volleyball Players' Spike Swing Based on Deep Learning
por: Hu, Lejun, et al.
Publicado: (2022)

The Application of Deep Learning on CBCT in Dentistry
por: Fan, Wenjie, et al.
Publicado: (2023)

Deep Learning and Its Applications in Biomedicine
por: Cao, Chensi, et al.
Publicado: (2018)

Human Microphysiological Systems and Organoids as in Vitro Models for Toxicological Studies
por: Truskey, George A.
Publicado: (2018)

Deep learning-enabled medical computer vision
por: Esteva, Andre, et al.
Publicado: (2021)

Deep Learning Concepts and Applications for Synthetic Biology
por: Beardall, William A.V., et al.
Publicado: (2022)

Computational Biomechanics of Sleep: A Systematic Mapping Review
por: Cheng, Ethan Shiu-Wang, et al.
Publicado: (2023)

A review on quantum computing and deep learning algorithms and their applications
por: Valdez, Fevrier, et al.
Publicado: (2022)

Intelligent Health Care: Applications of Deep Learning in Computational Medicine
por: Yang, Sijie, et al.
Publicado: (2021)

The application of computer-based quantitative analysis to explain carpal biomechanics
por: Sandow, Michael
Publicado: (2020)

Computational biomechanics for medicine: fundamental science and patient-specific applications
por: Doyle, Barry, et al.
Publicado: (2014)

Computational modeling in biomechanics
por: Mofrad, Mohammad
Publicado: (2010)

Application of Oxidative Stress to a Tissue-Engineered Vascular Aging Model Induces Endothelial Cell Senescence and Activation
por: Salmon, Ellen E., et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_170985dlsc09iibi55kpg4fudv