AIMC Topic: Finite Element Analysis

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Physics-guided machine learning for 3-D quantitative quasi-static elasticity imaging.

Physics in medicine and biology
We present a 3D extension of the Autoprogressive Method (AutoP) for quantitative quasi-static ultrasonic elastography (QUSE) based on sparse sampling of force-displacement measurements. Compared to current model-based inverse methods, our approach re...

A proposed soft pneumatic actuator control based on angle estimation from data-driven model.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
This article proposes a bending angle controller for soft pneumatic actuators, which could be implemented in soft robotic rehabilitation gloves to assist patients with hand impairment, such as stroke survivors. A data-driven model is used to estimate...

Laser-Induced Graphene for Electrothermally Controlled, Mechanically Guided, 3D Assembly and Human-Soft Actuators Interaction.

Advanced materials (Deerfield Beach, Fla.)
Mechanically guided, 3D assembly has attracted broad interests, owing to its compatibility with planar fabrication techniques and applicability to a diversity of geometries and length scales. Its further development requires the capability of on-dema...

A Comparative Classification Analysis of Abdominal Aortic Aneurysms by Machine Learning Algorithms.

Annals of biomedical engineering
The objective of this work was to perform image-based classification of abdominal aortic aneurysms (AAA) based on their demographic, geometric, and biomechanical attributes. We retrospectively reviewed existing demographics and abdominal computed tom...

A novel machine learning based computational framework for homogenization of heterogeneous soft materials: application to liver tissue.

Biomechanics and modeling in mechanobiology
Real-time simulation of organs increases comfort and safety for patients during the surgery. Proper generalized decomposition (PGD) is an efficient numerical method with coordinate errors below 1 mm and response time below 0.1 s that can be used for ...

A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves.

Scientific reports
Bioprosthetic heart valves (BHVs) are commonly used as heart valve replacements but they are prone to fatigue failure; estimating their remaining life directly from medical images is difficult. Analyzing the valve performance can provide better guida...

A feasibility study of deep learning for predicting hemodynamics of human thoracic aorta.

Journal of biomechanics
Numerical analysis methods including finite element analysis (FEA), computational fluid dynamics (CFD), and fluid-structure interaction (FSI) analysis have been used to study the biomechanics of human tissues and organs, as well as tissue-medical dev...

Interfacing Soft and Hard: A Spring Reinforced Actuator.

Soft robotics
Muscular hydrostats have long been a source of inspiration for soft robotic designs. With their inherent compliance, they excel in unpredictable environments and can gently manipulate objects with ease. However, their performance lacks where high for...

Simulation of hyperelastic materials in real-time using deep learning.

Medical image analysis
The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition, parallel ...

Neural network methodology for real-time modelling of bio-heat transfer during thermo-therapeutic applications.

Artificial intelligence in medicine
Real-time simulation of bio-heat transfer can improve surgical feedback in thermo-therapeutic treatment, leading to technical innovations to surgical process and improvements to patient outcomes; however, it is challenging to achieve real-time comput...