AI Medical Compendium Journal:
Journal of the mechanical behavior of biomedical materials

Showing 21 to 30 of 36 articles

A data-driven approach to characterizing nonlinear elastic behavior of soft materials.

Journal of the mechanical behavior of biomedical materials
The Autoprogressive (AutoP) method is a data-driven inverse method that leverages finite element analysis (FEA) and machine learning (ML) techniques to build constitutive relationships from measured force and displacement data. Previous applications ...

Insights into cell classification based on combination of multiple cellular mechanical phenotypes by using machine learning algorithm.

Journal of the mechanical behavior of biomedical materials
Although cellular elastic property (CEP, also known as cellular elastic modulus) has been frequently reported as a biomarker to distinguish some cancerous cells from their benign counterparts, it cannot be adopted as a universal hallmark to be applie...

Feedforward backpropagation artificial neural networks for predicting mechanical responses in complex nonlinear structures: A study on a long bone.

Journal of the mechanical behavior of biomedical materials
Feedforward backpropagation artificial neural networks (ANNs) have been increasingly employed in many engineering practices concerning materials modeling. Despite their extensive applications, how to achieve successfully trained ANNs is not thoroughl...

Model and parameter identification of soft tissue response to a movement of remotely navigated magnetic sphere.

Journal of the mechanical behavior of biomedical materials
Accurate and controlled movement of small, untethered objects within soft tissues has many potential applications in medical robotics. While medium reaction forces due to slow movement of solid objects in viscoelastic fluids are well-known, such forc...

ColGen: An end-to-end deep learning model to predict thermal stability of de novo collagen sequences.

Journal of the mechanical behavior of biomedical materials
Collagen is the most abundant structural protein in humans, with dozens of sequence variants accounting for over 30% of the protein in an animal body. The fibrillar and hierarchical arrangements of collagen are critical in providing mechanical proper...

A deep learning model for burn depth classification using ultrasound imaging.

Journal of the mechanical behavior of biomedical materials
Identification of burn depth with sufficient accuracy is a challenging problem. This paper presents a deep convolutional neural network to classify burn depth based on altered tissue morphology of burned skin manifested as texture patterns in the ult...

Microscale characterisation of the time-dependent mechanical behaviour of brain white matter.

Journal of the mechanical behavior of biomedical materials
Brain mechanics is a topic of deep interest because of the significant role of mechanical cues in both brain function and form. Specifically, capturing the heterogeneous and anisotropic behaviour of cerebral white matter (WM) is extremely challenging...

Can DXA image-based deep learning model predict the anisotropic elastic behavior of trabecular bone?

Journal of the mechanical behavior of biomedical materials
3D image-based finite element (FE) and bone volume fraction (BV/TV)/fabric tensor modeling techniques are currently used to determine the apparent stiffness tensor of trabecular bone for assessing its anisotropic elastic behavior. Inspired by the rec...

Deep learning approach to assess damage mechanics of bone tissue.

Journal of the mechanical behavior of biomedical materials
Machine learning methods have the potential to transform imaging techniques and analysis for healthcare applications with automation, making diagnostics and treatment more accurate and efficient, as well as to provide mechanistic insights into tissue...

What can artificial intelligence and machine learning tell us? A review of applications to equine biomechanical research.

Journal of the mechanical behavior of biomedical materials
Artificial intelligence (AI) and machine learning (ML) are fascinating interdisciplinary scientific domains where machines are provided with an approximation of human intelligence. The conjecture is that machines are able to learn from existing examp...