Synergistic Integration of Deep Neural Networks and Finite Element Method with Applications for Biomechanical Analysis of Human Aorta

Liang, Liang; Liu, Minliang; Elefteriades, John A.; Sun, Wei

doi:10.1101/2023.04.03.535423

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…In addition to our work with Dr. Rizzo, Nicolai Ostberg from our team, and Dr. Wei Sun and Dr. Liang Liang from Georgia Tech University and University of Miami, have analyzed our data using advanced Artificial Intelligence techniques, resulting in several new insights [5][6][7][8].…”

Section: Conventional "Big Data" Origins Of Aortic Institute Research...mentioning

confidence: 99%

“Big Data” Analyses Underlie Clinical Discoveries at the Aortic Institute

Zafar,

Ziganshin,

et al. 2023

Yale J Biol Med

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This issue of the Yale Journal of Biology and Medicine ( YJBM ) focuses on Big Data and precision analytics in medical research. At the Aortic Institute at Yale New Haven Hospital, the vast majority of our investigations have emanated from our large, prospective clinical database of patients with thoracic aortic aneurysm (TAA), supplemented by ultra-large genetic sequencing files. Among the fundamental clinical and scientific discoveries enabled by application of advanced statistical and artificial intelligence techniques on these clinical and genetic databases are the following: From analysis of Traditional “Big Data” (Large data sets) . 1. Ascending aortic aneurysms should be resected at 5 cm to prevent dissection and rupture. 2. Indexing aortic size to height improves aortic risk prognostication. 3. Aortic root dilatation is more malignant than mid-ascending aortic dilatation. 4. Ascending aortic aneurysm patients with bicuspid aortic valves do not carry the poorer prognosis previously postulated. 5. The descending and thoracoabdominal aorta are capable of rupture without dissection. 6. Female patients with TAA do more poorly than male patients. 7. Ascending aortic length is even better than aortic diameter at predicting dissection. 8. A “silver lining” of TAA disease is the profound, lifelong protection from atherosclerosis. From Modern “Big Data” Machine Learning/Artificial Intelligence analysis : 1. Machine learning models for TAA: outperforming traditional anatomic criteria. 2. Genetic testing for TAA and dissection and discovery of novel causative genes. 3. Phenotypic genetic characterization by Artificial Intelligence. 4. Panel of RNAs “detects” TAA. Such findings, based on (a) long-standing application of advanced conventional statistical analysis to large clinical data sets, and (b) recent application of advanced machine learning/artificial intelligence to large genetic data sets at the Yale Aortic Institute have advanced the diagnosis and medical and surgical treatment of TAA.

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Section: Conventional "Big Data" Origins Of Aortic Institute Research...mentioning

confidence: 99%

“Big Data” Analyses Underlie Clinical Discoveries at the Aortic Institute

Zafar,

Ziganshin,

et al. 2023

Yale J Biol Med

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Interpreting and generalizing deep learning in physics-based problems with functional linear models

Arzani,

Yuan,

Newell

et al. 2024

Engineering with Computers

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Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models

Caforio,

Regazzoni,

Pagani

et al. 2024

Comput Mech

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The development of biophysical models for clinical applications is rapidly advancing in the research community, thanks to their predictive nature and their ability to assist the interpretation of clinical data. However, high-resolution and accurate multi-physics computational models are computationally expensive and their personalisation involves fine calibration of a large number of parameters, which may be space-dependent, challenging their clinical translation. In this work, we propose a new approach, which relies on the combination of physics-informed neural networks (PINNs) with three-dimensional soft tissue nonlinear biomechanical models, capable of reconstructing displacement fields and estimating heterogeneous patient-specific biophysical properties and secondary variables such as stresses and strains. The proposed learning algorithm encodes information from a limited amount of displacement and, in some cases, strain data, that can be routinely acquired in the clinical setting, and combines it with the physics of the problem, represented by a mathematical model based on partial differential equations, to regularise the problem and improve its convergence properties. Several benchmarks are presented to show the accuracy and robustness of the proposed method with respect to noise and model uncertainty and its great potential to enable the effective identification of patient-specific, heterogeneous physical properties, e.g. tissue stiffness properties. In particular, we demonstrate the capability of PINNs to detect the presence, location and severity of scar tissue, which is beneficial to develop personalised simulation models for disease diagnosis, especially for cardiac applications.

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Synergistic Integration of Deep Neural Networks and Finite Element Method with Applications for Biomechanical Analysis of Human Aorta

Cited by 3 publications

References 33 publications

“Big Data” Analyses Underlie Clinical Discoveries at the Aortic Institute

“Big Data” Analyses Underlie Clinical Discoveries at the Aortic Institute

Interpreting and generalizing deep learning in physics-based problems with functional linear models

Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models

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