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Villa B, Erranz B, Cruces P, Retamal J, Hurtado DE. Mechanical and morphological characterization of the emphysematous lung tissue. Acta Biomater 2024; 181:282-296. [PMID: 38705223 DOI: 10.1016/j.actbio.2024.04.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 04/22/2024] [Accepted: 04/25/2024] [Indexed: 05/07/2024]
Abstract
Irreversible alveolar airspace enlargement is the main characteristic of pulmonary emphysema, which has been extensively studied using animal models. While the alterations in lung mechanics associated with these morphological changes have been documented in the literature, the study of the mechanical behavior of parenchymal tissue from emphysematous lungs has been poorly investigated. In this work, we characterize the mechanical and morphological properties of lung tissue in elastase-induced emphysema rat models under varying severity conditions. We analyze the non-linear tissue behavior using suitable hyperelastic constitutive models that enable to compare different non-linear responses in terms of hyperelastic material parameters. We further analyze the effect of the elastase dose on alveolar morphology and tissue material parameters and study their connection with respiratory-system mechanical parameters. Our results show that while the lung mechanical function is not significantly influenced by the elastase treatment, the tissue mechanical behavior and alveolar morphology are markedly affected by it. We further show a strong association between alveolar enlargement and tissue softening, not evidenced by respiratory-system compliance. Our findings highlight the importance of understanding tissue mechanics in emphysematous lungs, as changes in tissue properties could detect the early stages of emphysema remodeling. STATEMENT OF SIGNIFICANCE: Gas exchange is vital for life and strongly relies on the mechanical function of the lungs. Pulmonary emphysema is a prevalent respiratory disease where alveolar walls are damaged, causing alveolar enlargement that induces harmful changes in the mechanical response of the lungs. In this work, we study how the mechanical properties of lung tissue change during emphysema. Our results from animal models show that tissue properties are more sensitive to alveolar enlargement due to emphysema than other mechanical properties that describe the function of the whole respiratory system.
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Affiliation(s)
- Benjamín Villa
- Department of Structural and Geotechnical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, Vicuña Mackenna 4860, Santiago, Chile; Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Santiago, Chile
| | - Benjamín Erranz
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Santiago, Chile
| | - Pablo Cruces
- Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile. Avenida Repblica 440, Santiago, Chile
| | - Jaime Retamal
- Departamento de Medicina Intensiva, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile, Santiago, Chile
| | - Daniel E Hurtado
- Department of Structural and Geotechnical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, Vicuña Mackenna 4860, Santiago, Chile; Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Santiago, Chile; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, 02140, USA.
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Barahona J, Sahli Costabal F, Hurtado DE. Machine learning modeling of lung mechanics: Assessing the variability and propagation of uncertainty in respiratory-system compliance and airway resistance. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107888. [PMID: 37948910 DOI: 10.1016/j.cmpb.2023.107888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/12/2023] [Accepted: 10/25/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Traditional assessment of patient response in mechanical ventilation relies on respiratory-system compliance and airway resistance. Clinical evidence has shown high variability in these parameters, highlighting the difficulty of predicting them before the start of ventilation therapy. This motivates the creation of computational models that can connect structural and tissue features with lung mechanics. In this work, we leverage machine learning (ML) techniques to construct predictive lung function models informed by non-linear finite element simulations, and use them to investigate the propagation of uncertainty in the lung mechanical response. METHODS We revisit a continuum poromechanical formulation of the lungs suitable for determining patient response. Based on this framework, we create high-fidelity finite element models of human lungs from medical images. We also develop a low-fidelity model based on an idealized sphere geometry. We then use these models to train and validate three ML architectures: single-fidelity and multi-fidelity Gaussian process regression, and artificial neural networks. We use the best predictive ML model to further study the sensitivity of lung response to variations in tissue structural parameters and boundary conditions via sensitivity analysis and forward uncertainty quantification. Codes are available for download at https://github.com/comp-medicine-uc/ML-lung-mechanics-UQ RESULTS: The low-fidelity model delivers a lung response very close to that predicted by high-fidelity simulations and at a fraction of the computational time. Regarding the trained ML models, the multi-fidelity GP model consistently delivers better accuracy than the single-fidelity GP and neural network models in estimating respiratory-system compliance and resistance (R2∼0.99). In terms of computational efficiency, our ML model delivers a massive speed-up of ∼970,000× with respect to high-fidelity simulations. Regarding lung function, we observed an almost matched and non-linear behavior between specific structural parameters and chest wall stiffness with compliance. Also, we observed a strong modulation of airways resistance with tissue permeability. CONCLUSIONS Our findings unveil the relevance of specific lung tissue parameters and boundary conditions in the respiratory-system response. Furthermore, we highlight the advantages of adopting a multi-fidelity ML approach that combines data from different levels to yield accurate and efficient estimates of clinical mechanical markers. We envision that the methods presented here can open the way to the development of predictive ML models of the lung response that can inform clinical decisions.
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Affiliation(s)
- José Barahona
- Department of Structural and Geotechnical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, 7820436, Chile; Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, 7820436, Chile
| | - Francisco Sahli Costabal
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, 7820436, Chile; Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, 7820436, Chile
| | - Daniel E Hurtado
- Department of Structural and Geotechnical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, 7820436, Chile; Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, 7820436, Chile; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, 02140, USA.
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Li Z, Pei Y, Wang Y, Tian Q. An enhanced respiratory mechanics model based on double-exponential and fractional calculus. Front Physiol 2023; 14:1273645. [PMID: 38111899 PMCID: PMC10726035 DOI: 10.3389/fphys.2023.1273645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 11/17/2023] [Indexed: 12/20/2023] Open
Abstract
We address mathematical modelling of respiratory mechanics and put forward a model based on double-exponential and fractional calculus for parameter estimation, model simulation, and evaluation based on actual data. Our model has been implemented on a publicly available executable code with adjustable parameters, making it suitable for different applications. Our analysis represents the first application of fractional calculus and double-exponential modelling to respiratory mechanics, and allows us to propose a hybrid model fitting experimental data in different ventilation modes. Furthermore, our model can be used to study the mechanical features of the respiratory system, improve the safety of ventilation techniques, reduce ventilation damages, and provide strong support for fast and adaptive determination of ventilation parameters.
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Affiliation(s)
- Zongwei Li
- Department of Thoracic Surgery, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Yanbin Pei
- Department of Thoracic Surgery, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
- Medical School of Chinese PLA, Beijing, China
| | - Yuqi Wang
- Department of Thoracic Surgery, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Qing Tian
- Department of Thoracic Surgery, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
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Daphalapurkar N, Riglin J, Mohan A, Harris J, Bernardin J. Quasi-dynamic breathing model of the lung incorporating viscoelasticity of the lung tissue. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2023:e3744. [PMID: 37334440 DOI: 10.1002/cnm.3744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 03/21/2023] [Accepted: 06/05/2023] [Indexed: 06/20/2023]
Abstract
We advanced a novel model to calculate viscoelastic lung compliance and airflow resistance in presence of mucus, accounting for the quasi-linear viscoelastic stress-strain response of the parenchyma (alveoli) tissue. We adapted a continuum-based numerical modeling approach for the lung, integrating the fluid mechanics of the airflow within individual generations of the bronchi and alveoli. The model accounts for elasticity of the deformable bronchioles, resistance to airflow due to the presence of mucus within the bronchioles, and subsequent mucus flow. Simulated quasi-dynamic inhalation and expiration cycles were used to characterize the net compliance and resistance of the lung, considering the rheology of the mucus and viscoelastic properties of the parenchyma tissue. The structure and material properties of the lung were identified to have an important contribution to the lung compliance and airflow resistance. The secondary objective of this work was to assess whether a higher frequency and smaller volume of harmonic air flow rate compared to a normal ventilator breathing cycle enhanced mucus outflow. Results predict, lower mucus viscosity and higher excitation frequency of breathing are favorable for the flow of mucus up the bronchi tree, towards the trachea.
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Affiliation(s)
- Nitin Daphalapurkar
- Fluid Dynamics and Solid Mechanics, T-3, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
| | - Jacob Riglin
- Mechanical and Thermal Engineering, E-1, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
| | - Arvind Mohan
- Computational Physics and Methods, CCS-2, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
| | - Jennifer Harris
- Biosecurity and Public Health, B-10, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
| | - John Bernardin
- Mechanical and Thermal Engineering, E-1, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
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