ArticleActa biomaterialia2017
A deep learning approach to estimate chemically-treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images.
Article in Acta biomaterialia, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 64 citations in OpenAlex.
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- SEMPro: A Data-Driven Pipeline To Learn Structure-Property Insights from Scanning Electron Microscopy Images.ACS materials letters · 2023Article
- The Potential of Deep Learning to Advance Clinical Applications of Computational Biomechanics.Bioengineering (Basel, Switzerland) · 2023Review
- Toward next-generation endoscopes integrating biomimetic video systems, nonlinear optical microscopy, and deep learning.Biophysics reviews · 2023Review
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- An interpretable deep learning approach for designing nanoporous silicon nitride membranes with tunable mechanical properties.npj computational materials · 2023Article
- Two-Photon Imaging for Non-Invasive Corneal Examination.Sensors (Basel, Switzerland) · 2022Review
- Interplay between Artificial Intelligence and Biomechanics Modeling in the Cardiovascular Disease Prediction.Biomedicines · 2022Review
- PSHG-TISS: A collection of polarization-resolved second harmonic generation microscopy images of fixed tissues.Scientific data · 2022Article
- Deep-learning-based 3D cellular force reconstruction directly from volumetric images.Biophysical journal · 2022Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Recurrent neural network to predict hyperelastic constitutive behaviors of the skeletal muscle.Medical & biological engineering & computing · 2022Article
- Predictive constitutive modelling of arteries by deep learning.Journal of the Royal Society, Interface · 2021Article
- Unraveling the Local Relation Between Tissue Composition and Human Brain Mechanics Through Machine Learning.Frontiers in bioengineering and biotechnology · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Biological collagenous tissues comprised of networks of collagen fibers are suitable for a broad spectrum of medical applications owing to their attractive mechanical properties. In this study, we developed a noninvasive approach to estimate collagenous tissue elastic properties directly from microscopy images using Machine Learning (ML) techniques. Glutaraldehyde-treated bovine pericardium (GLBP) tissue, widely used in the fabrication of bioprosthetic heart valves and vascular patches, was chosen to develop a representative application. A Deep Learning model was designed and trained to process second harmonic generation (SHG) images of collagen networks in GLBP tissue samples, and directly predict the tissue elastic mechanical properties. The trained model is capable of identifying the overall tissue stiffness with a classification accuracy of 84%, and predicting the nonlinear anisotropic stress-strain curves with average regression errors of 0.021 and 0.031. Thus, this study demonstrates the feasibility and great potential of using the Deep Learning approach for fast and noninvasive assessment of collagenous tissue elastic properties from microstructural images. STATEMENT OF SIGNIFICANCE: In this study, we developed, to our best knowledge, the first Deep Learning-based approach to estimate the elastic properties of collagenous tissues directly from noninvasive second harmonic generation images. The success of this study holds promise for the use of Machine Learning techniques to noninvasively and efficiently estimate the mechanical properties of many structure-based biological materials, and it also enables many potential applications such as serving as a quality control tool to select tissue for the manufacturing of medical devices (e.g. bioprosthetic heart valves).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.