High-Speed High-Fidelity Cardiac Simulations Using a Neural Network Finite Element Approach

Motiwale, Shruti; Zhang, Wenbo; Sacks, Michael S.

doi:10.1007/978-3-031-35302-4_55

Cited by 3 publications

(1 citation statement)

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“…Taking advantage of the ability of neural networks to perform automatic differentiation, neural networks were used in a cardiac model to compute the displacement field from pressure and active contraction inputs [76]. Both a simplified model and a complete mechanical model were considered for the training.…”

Section: Physics-informed Neural Network (Pinn)mentioning

confidence: 99%

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

Truskey

2023

Bioengineering

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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 is most advanced in cardiovascular applications but can be applied to other tissues. The challenges for advancing computational biomechanics for real-time patient diagnostics and treatment include errors and missing information in the patient data, the large computational requirements for the numerical solutions to multiscale biomechanical equations, and the uncertainty over boundary conditions and constitutive relations. This review summarizes current efforts to use deep learning to address these challenges and integrate large data sets and computational methods to enable real-time clinical information. Examples are drawn from cardiovascular fluid mechanics, soft-tissue mechanics, and bone biomechanics. The application of deep-learning convolutional neural networks can reduce the time taken to complete image segmentation, and meshing and solution of finite element models, as well as improving the accuracy of inlet and outlet conditions. Such advances are likely to facilitate the adoption of these models to aid in the assessment of the severity of cardiovascular disease and the development of new surgical treatments.

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Section: Physics-informed Neural Network (Pinn)mentioning

confidence: 99%