A deep learning approach to estimate chemically-treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images

Liang, Liang; Liu, Minliang; Sun, Wei

doi:10.1016/j.actbio.2017.09.025

Cited by 54 publications

(35 citation statements)

References 56 publications

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“…In the application area of biomechanics, most of the existing works use simple deep learning or physics-consistent deep learning methods. Specifically, these methods have been applied for modeling the aorta and estimating the stress fields 52–54 or estimating the constitutive model parameters for aortic wall 55,56 . For BHVs, while there are optimization based methods for design of transcatheter aortic valves 57,58 , machine learning methods have mainly been used for 3D reconstruction of the valve geometry 59 .…”

Section: Methodsmentioning

confidence: 99%

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

Balu

Nallagonda

et al. 2019

Sci Rep

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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 guidance for personalized valve design. However, such analyses are often computationally intensive. In this work, we introduce the concept of deep learning (DL) based finite element analysis (DLFEA) to learn the deformation biomechanics of bioprosthetic aortic valves directly from simulations. The proposed DL framework can eliminate the time-consuming biomechanics simulations, while predicting valve deformations with the same fidelity. We present statistical results that demonstrate the high performance of the DLFEA framework and the applicability of the framework to predict bioprosthetic aortic valve deformations. With further development, such a tool can provide fast decision support for designing surgical bioprosthetic aortic valves. Ultimately, this framework could be extended to other BHVs and improve patient care.

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Section: Methodsmentioning

confidence: 99%

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

Balu

Nallagonda

et al. 2019

Sci Rep

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“…A deep learning method has been employed to non-invasively detect chemically treated collagenous tissue nonlinear anisotropic stress-strain responses in the microscopic images [77]. VGG16 is used for the prognosis of glioblastoma and as a radiographic biomarker for noninvasive categorization between true progression and pseudo-progression in these patients [78].…”

Section: Deep Thermomicsmentioning

confidence: 99%

Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics

2020

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Breast cancer is the most common cancer in women. Early diagnosis improves outcome and survival, which is the cornerstone of breast cancer treatment. Thermography has been utilized as a complementary diagnostic technique in breast cancer detection. Artificial intelligence (AI) has the capacity to capture and analyze the entire concealed information in thermography. In this study, we propose a method to potentially detect the immunohistochemical response to breast cancer by finding thermal heterogeneous patterns in the targeted area. In this study for breast cancer screening 208 subjects participated and normal and abnormal (diagnosed by mammography or clinical diagnosis) conditions were analyzed. High-dimensional deep thermomic features were extracted from the ResNet-50 pre-trained model from low-rank thermal matrix approximation using sparse principal component analysis. Then, a sparse deep autoencoder designed and trained for such data decreases the dimensionality to 16 latent space thermomic features. A random forest model was used to classify the participants. The proposed method preserves thermal heterogeneity, which leads to successful classification between normal and abnormal subjects with an accuracy of 78.16% (73.3–81.07%). By non-invasively capturing a thermal map of the entire tumor, the proposed method can assist in screening and diagnosing this malignancy. These thermal signatures may preoperatively stratify the patients for personalized treatment planning and potentially monitor the patients during treatment.

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“…In the other case, the corresponding metric is the normalized M AE (N M AE), presented in equation 4 as a function of the MAE 43,49,57,58 .…”

Section: Input and Output Featuresmentioning

confidence: 99%

Real‐time biomechanics using the finite element method and machine learning: Review and perspective

et al. 2020

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The finite element method (FEM) is the preferred method to simulate phenomena in anatomical structures. However, purely FEM-based mechanical simulations require considerable time, limiting their use in clinical applications that require real-time responses, such as haptics simulators. Machine learning (ML) approaches have been proposed to help with the reduction of the required time. The present paper reviews cases where ML could help to generate faster simulations, without considerably affecting the performance results. Methods: This review details the ML approaches used, considering the anatomical structures involved, the data collection strategies, the selected ML algorithms, with corresponding features, the metrics used for validation, and the resulting time gains. Results: A total of 41 references were found. ML algorithms are mainly trained with FEM-based simulations, in 32 publications. The preferred ML approach is neural networks, including deep learning, in 35 publications. Tissue deformation is simulated in 18 applications, but other features are also considered. The average distance error and mean squared error are the most frequently used performance metrics, in 14 and 17 publications, respectively. The time gains were considerable, going from hours or minutes for purely FEM-based simulations to milliseconds, when using ML. Conclusions: ML algorithms can be used to accelerate FEM-based biomechanical simulations of anatomical structures, possibly reaching real-time responses. Fast and real-time simulations of anatomical structures, generated with ML algorithms, can help to reduce the time required by FEM-based simulations and accelerate their adoption in the clinical practice.

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A deep learning approach to estimate chemically-treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images

Cited by 54 publications

References 56 publications

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

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

Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics

Real‐time biomechanics using the finite element method and machine learning: Review and perspective

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