1
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Ebrahimian A, Mohammadi H, Maftoon N. Material characterization of human middle ear using machine-learning-based surrogate models. J Mech Behav Biomed Mater 2024; 153:106478. [PMID: 38493562 DOI: 10.1016/j.jmbbm.2024.106478] [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: 09/14/2023] [Revised: 02/09/2024] [Accepted: 02/24/2024] [Indexed: 03/19/2024]
Abstract
This study aims to introduce a novel non-invasive method for rapid material characterization of middle-ear structures, taking into consideration the invaluable insights provided by the mechanical properties of ear tissues. Valuable insights into various ear pathologies can be gleaned from the mechanical properties of ear tissues, yet conventional techniques for assessing these properties often entail invasive procedures that preclude their use on living patients. In this study, in the first step, we developed machine-learning models of the middle ear to predict its responses with a significantly lower computational cost in comparison to finite-element models. Leveraging findings from prior research, we focused on the most influential model parameters: the Young's modulus and thickness of the tympanic membrane and the Young's modulus of the stapedial annular ligament. The eXtreme Gradient Boosting (XGBoost) method was implemented for creating the machine-learning models. Subsequently, we combined the created machine-learning models with Bayesian optimization (BoTorch) for fast and efficient estimation of the Young's moduli of the tympanic membrane and the stapedial annular ligament. We demonstrate that the resultant surrogate models can fairly represent the vibrational responses of the umbo, stapes footplate, and vibration patterns of the tympanic membrane at most frequencies. Also, our proposed material characterization approach successfully estimated the Young's moduli of the tympanic membrane and stapedial annular ligament (separately and simultaneously) with values of mean absolute percentage error of less than 7%. The remarkable accuracy achieved through the proposed material characterization method underscores its potential for eventual clinical applications of estimating mechanical properties of the middle-ear structures for diagnostic purposes.
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Affiliation(s)
- Arash Ebrahimian
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada; Centre for Bioengineering and Biotechnology, University of Waterloo, Waterloo, ON, Canada
| | - Hossein Mohammadi
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada; Centre for Bioengineering and Biotechnology, University of Waterloo, Waterloo, ON, Canada
| | - Nima Maftoon
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada; Centre for Bioengineering and Biotechnology, University of Waterloo, Waterloo, ON, Canada.
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2
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Samant S, Bakhos JJ, Wu W, Zhao S, Kassab GS, Khan B, Panagopoulos A, Makadia J, Oguz UM, Banga A, Fayaz M, Glass W, Chiastra C, Burzotta F, LaDisa JF, Iaizzo P, Murasato Y, Dubini G, Migliavacca F, Mickley T, Bicek A, Fontana J, West NEJ, Mortier P, Boyers PJ, Gold JP, Anderson DR, Tcheng JE, Windle JR, Samady H, Jaffer FA, Desai NR, Lansky A, Mena-Hurtado C, Abbott D, Brilakis ES, Lassen JF, Louvard Y, Stankovic G, Serruys PW, Velazquez E, Elias P, Bhatt DL, Dangas G, Chatzizisis YS. Artificial Intelligence, Computational Simulations, and Extended Reality in Cardiovascular Interventions. JACC Cardiovasc Interv 2023; 16:2479-2497. [PMID: 37879802 DOI: 10.1016/j.jcin.2023.07.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 07/11/2023] [Accepted: 07/13/2023] [Indexed: 10/27/2023]
Abstract
Artificial intelligence, computational simulations, and extended reality, among other 21st century computational technologies, are changing the health care system. To collectively highlight the most recent advances and benefits of artificial intelligence, computational simulations, and extended reality in cardiovascular therapies, we coined the abbreviation AISER. The review particularly focuses on the following applications of AISER: 1) preprocedural planning and clinical decision making; 2) virtual clinical trials, and cardiovascular device research, development, and regulatory approval; and 3) education and training of interventional health care professionals and medical technology innovators. We also discuss the obstacles and constraints associated with the application of AISER technologies, as well as the proposed solutions. Interventional health care professionals, computer scientists, biomedical engineers, experts in bioinformatics and visualization, the device industry, ethics committees, and regulatory agencies are expected to streamline the use of AISER technologies in cardiovascular interventions and medicine in general.
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Affiliation(s)
- Saurabhi Samant
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Jules Joel Bakhos
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Wei Wu
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Shijia Zhao
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Ghassan S Kassab
- California Medical Innovations Institute, San Diego, California, USA
| | - Behram Khan
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Anastasios Panagopoulos
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Janaki Makadia
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Usama M Oguz
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Akshat Banga
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Muhammad Fayaz
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - William Glass
- Interprofessional Experiential Center for Enduring Learning, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Claudio Chiastra
- PoliTo(BIO)Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
| | - Francesco Burzotta
- Department of Cardiovascular Sciences, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - John F LaDisa
- Departments of Biomedical Engineering and Pediatrics - Division of Cardiology, Herma Heart Institute, Children's Wisconsin and the Medical College of Wisconsin, and the MARquette Visualization Lab, Marquette University, Milwaukee, Wisconsin, USA
| | - Paul Iaizzo
- Visible Heart Laboratories, Department of Surgery, University of Minnesota, Minnesota, USA
| | - Yoshinobu Murasato
- Department of Cardiology, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan
| | - Gabriele Dubini
- Department of Chemistry, Materials and Chemical Engineering 'Giulio Natta', Politecnico di Milano, Milan, Italy
| | - Francesco Migliavacca
- Department of Chemistry, Materials and Chemical Engineering 'Giulio Natta', Politecnico di Milano, Milan, Italy
| | | | - Andrew Bicek
- Boston Scientific Inc, Marlborough, Massachusetts, USA
| | | | | | | | - Pamela J Boyers
- Interprofessional Experiential Center for Enduring Learning, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Jeffrey P Gold
- Interprofessional Experiential Center for Enduring Learning, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Daniel R Anderson
- Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - James E Tcheng
- Cardiovascular Division, Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina, USA
| | - John R Windle
- Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Habib Samady
- Georgia Heart Institute, Gainesville, Georgia, USA
| | - Farouc A Jaffer
- Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Nihar R Desai
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Alexandra Lansky
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Carlos Mena-Hurtado
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Dawn Abbott
- Cardiovascular Institute, Warren Alpert Medical School at Brown University, Providence, Rhode Island, USA
| | - Emmanouil S Brilakis
- Center for Advanced Coronary Interventions, Minneapolis Heart Institute, Minneapolis, Minnesota, USA
| | - Jens Flensted Lassen
- Department of Cardiology B, Odense University Hospital, Odense, Syddanmark, Denmark
| | - Yves Louvard
- Institut Cardiovasculaire Paris Sud, Massy, France
| | - Goran Stankovic
- Department of Cardiology, Clinical Center of Serbia, Belgrade, Serbia
| | - Patrick W Serruys
- Department of Cardiology, National University of Ireland, Galway, Galway, Ireland
| | - Eric Velazquez
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Pierre Elias
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, New York, New York, USA
| | - Deepak L Bhatt
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - George Dangas
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yiannis S Chatzizisis
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA.
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3
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Rabbani A, Gao H, Lazarus A, Dalton D, Ge Y, Mangion K, Berry C, Husmeier D. Image-based estimation of the left ventricular cavity volume using deep learning and Gaussian process with cardio-mechanical applications. Comput Med Imaging Graph 2023; 106:102203. [PMID: 36848766 DOI: 10.1016/j.compmedimag.2023.102203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 11/26/2022] [Accepted: 02/17/2023] [Indexed: 02/27/2023]
Abstract
In this investigation, an image-based method has been developed to estimate the volume of the left ventricular cavity using cardiac magnetic resonance (CMR) imaging data. Deep learning and Gaussian processes have been applied to bring the estimations closer to the cavity volumes manually extracted. CMR data from 339 patients and healthy volunteers have been used to train a stepwise regression model that can estimate the volume of the left ventricular cavity at the beginning and end of diastole. We have decreased the root mean square error (RMSE) of cavity volume estimation approximately from 13 to 8 ml compared to the common practice in the literature. Considering the RMSE of manual measurements is approximately 4 ml on the same dataset, 8 ml of error is notable for a fully automated estimation method, which needs no supervision or user-hours once it has been trained. Additionally, to demonstrate a clinically relevant application of automatically estimated volumes, we inferred the passive material properties of the myocardium given the volume estimates using a well-validated cardiac model. These material properties can be further used for patient treatment planning and diagnosis.
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Affiliation(s)
- Arash Rabbani
- School of Mathematics & Statistics, University of Glasgow, Glasgow G12 8QQ, United Kingdom; School of Computing, University of Leeds, Leeds LS2 9JT, United Kingdom.
| | - Hao Gao
- School of Mathematics & Statistics, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Alan Lazarus
- School of Mathematics & Statistics, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - David Dalton
- School of Mathematics & Statistics, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Yuzhang Ge
- School of Mathematics & Statistics, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Kenneth Mangion
- School of Cardiovascular & Metabolic Health, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Colin Berry
- School of Cardiovascular & Metabolic Health, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Dirk Husmeier
- School of Mathematics & Statistics, University of Glasgow, Glasgow G12 8QQ, United Kingdom
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4
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Lazarus A, Dalton D, Husmeier D, Gao H. Sensitivity analysis and inverse uncertainty quantification for the left ventricular passive mechanics. Biomech Model Mechanobiol 2022; 21:953-982. [PMID: 35377030 PMCID: PMC9132878 DOI: 10.1007/s10237-022-01571-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 02/28/2022] [Indexed: 01/08/2023]
Abstract
Personalized computational cardiac models are considered to be a unique and powerful tool in modern cardiology, integrating the knowledge of physiology, pathology and fundamental laws of mechanics in one framework. They have the potential to improve risk prediction in cardiac patients and assist in the development of new treatments. However, in order to use these models for clinical decision support, it is important that both the impact of model parameter perturbations on the predicted quantities of interest as well as the uncertainty of parameter estimation are properly quantified, where the first task is a priori in nature (meaning independent of any specific clinical data), while the second task is carried out a posteriori (meaning after specific clinical data have been obtained). The present study addresses these challenges for a widely used constitutive law of passive myocardium (the Holzapfel-Ogden model), using global sensitivity analysis (SA) to address the first challenge, and inverse-uncertainty quantification (I-UQ) for the second challenge. The SA is carried out on a range of different input parameters to a left ventricle (LV) model, making use of computationally efficient Gaussian process (GP) surrogate models in place of the numerical forward simulator. The results of the SA are then used to inform a low-order reparametrization of the constitutive law for passive myocardium under consideration. The quality of this parameterization in the context of an inverse problem having observed noisy experimental data is then quantified with an I-UQ study, which again makes use of GP surrogate models. The I-UQ is carried out in a Bayesian manner using Markov Chain Monte Carlo, which allows for full uncertainty quantification of the material parameter estimates. Our study reveals insights into the relation between SA and I-UQ, elucidates the dependence of parameter sensitivity and estimation uncertainty on external factors, like LV cavity pressure, and sheds new light on cardio-mechanic model formulation, with particular focus on the Holzapfel-Ogden myocardial model.
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Affiliation(s)
- Alan Lazarus
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - David Dalton
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Dirk Husmeier
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Hao Gao
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
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5
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Borowska A, Gao H, Lazarus A, Husmeier D. Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3593. [PMID: 35302293 PMCID: PMC9285944 DOI: 10.1002/cnm.3593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 03/12/2022] [Indexed: 06/14/2023]
Abstract
We consider parameter inference in cardio-mechanic models of the left ventricle, in particular the one based on the Holtzapfel-Ogden (HO) constitutive law, using clinical in vivo data. The equations underlying these models do not admit closed form solutions and hence need to be solved numerically. These numerical procedures are computationally expensive making computational run times associated with numerical optimisation or sampling excessive for the uptake of the models in the clinical practice. To address this issue, we adopt the framework of Bayesian optimisation (BO), which is an efficient statistical technique of global optimisation. BO seeks the optimum of an unknown black-box function by sequentially training a statistical surrogate-model and using it to select the next query point by leveraging the associated exploration-exploitation trade-off. To guarantee that the estimates based on the in vivo data are realistic also for high-pressures, unobservable in vivo, we include a penalty term based on a previously published empirical law developed using ex vivo data. Two case studies based on real data demonstrate that the proposed BO procedure outperforms the state-of-the-art inference algorithm for the HO constitutive law.
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Affiliation(s)
| | - Hao Gao
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowUK
| | - Alan Lazarus
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowUK
| | - Dirk Husmeier
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowUK
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6
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Lazarus A, Gao H, Luo X, Husmeier D. Improving cardio‐mechanic inference by combining in vivo strain data with ex vivo volume–pressure data. J R Stat Soc Ser C Appl Stat 2022. [DOI: 10.1111/rssc.12560] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | - Hao Gao
- University of Glasgow GlasgowUK
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7
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Guan D, Mei Y, Xu L, Cai L, Luo X, Gao H. Effects of dispersed fibres in myocardial mechanics, Part I: passive response. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:3972-3993. [PMID: 35341283 DOI: 10.3934/mbe.2022183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
It is widely acknowledged that an imbalanced biomechanical environment can have significant effects on myocardial pathology, leading to adverse remodelling of cardiac function if it persists. Accurate stress prediction essentially depends on the strain energy function which should have competent descriptive and predictive capabilities. Previous studies have focused on myofibre dispersion, but not on fibres along other directions. In this study, we will investigate how fibre dispersion affects myocardial biomechanical behaviours by taking into account both the myofibre dispersion and the sheet fibre dispersion, with a focus on the sheet fibre dispersion. Fibre dispersion is incorporated into a widely-used myocardial strain energy function using the discrete fibre bundle approach. We first study how different dispersion affects the descriptive capability of the strain energy function when fitting to ex vivo experimental data, and then the predictive capability in a human left ventricle during diastole. Our results show that the chosen strain energy function can achieve the best goodness-of-fit to the experimental data by including both fibre dispersion. Furthermore, noticeable differences in stress can be found in the LV model. Our results may suggest that it is necessary to include both dispersion for myofibres and the sheet fibres for the improved descriptive capability to the ex vivo experimental data and potentially more accurate stress prediction in cardiac mechanics.
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Affiliation(s)
- Debao Guan
- School of Mathematics and Statistics, University of Glasgow, UK
| | - Yuqian Mei
- School of Medical Imaging, North Sichuan Medical College, Sichuan, China
| | - Lijian Xu
- Centre for Perceptual and Interactive Intelligence, The Chinese University of Hong Kong, Hong Kong, China
| | - Li Cai
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, China
| | - Xiaoyu Luo
- School of Mathematics and Statistics, University of Glasgow, UK
| | - Hao Gao
- School of Mathematics and Statistics, University of Glasgow, UK
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8
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Maso Talou GD, Babarenda Gamage TP, Nash MP. Efficient Ventricular Parameter Estimation Using AI-Surrogate Models. Front Physiol 2021; 12:732351. [PMID: 34721062 PMCID: PMC8551833 DOI: 10.3389/fphys.2021.732351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 09/17/2021] [Indexed: 12/02/2022] Open
Abstract
The onset and progression of pathological heart conditions, such as cardiomyopathy or heart failure, affect its mechanical behaviour due to the remodelling of the myocardial tissues to preserve its functional response. Identification of the constitutive properties of heart tissues could provide useful biomarkers to diagnose and assess the progression of disease. We have previously demonstrated the utility of efficient AI-surrogate models to simulate passive cardiac mechanics. Here, we propose the use of this surrogate model for the identification of myocardial mechanical properties and intra-ventricular pressure by solving an inverse problem with two novel AI-based approaches. Our analysis concluded that: (i) both approaches were robust toward Gaussian noise when the ventricle data for multiple loading conditions were combined; and (ii) estimates of one and two parameters could be obtained in less than 9 and 18 s, respectively. The proposed technique yields a viable option for the translation of cardiac mechanics simulations and biophysical parameter identification methods into the clinic to improve the diagnosis and treatment of heart pathologies. In addition, the proposed estimation techniques are general and can be straightforwardly translated to other applications involving different anatomical structures.
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Affiliation(s)
- Gonzalo D Maso Talou
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | | | - Martyn P Nash
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.,Department of Engineering Science, University of Auckland, Auckland, New Zealand
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9
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Menichetti A, Bartsoen L, Depreitere B, Vander Sloten J, Famaey N. A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion. Front Bioeng Biotechnol 2021; 9:714128. [PMID: 34692652 PMCID: PMC8531645 DOI: 10.3389/fbioe.2021.714128] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 08/10/2021] [Indexed: 11/13/2022] Open
Abstract
Controlled cortical impact (CCI) on porcine brain is often utilized to investigate the pathophysiology and functional outcome of focal traumatic brain injury (TBI), such as cerebral contusion (CC). Using a finite element (FE) model of the porcine brain, the localized brain strain and strain rate resulting from CCI can be computed and compared to the experimentally assessed cortical lesion. This way, tissue-level injury metrics and corresponding thresholds specific for CC can be established. However, the variability and uncertainty associated with the CCI experimental parameters contribute to the uncertainty of the provoked cortical lesion and, in turn, of the predicted injury metrics. Uncertainty quantification via probabilistic methods (Monte Carlo simulation, MCS) requires a large number of FE simulations, which results in a time-consuming process. Following the recent success of machine learning (ML) in TBI biomechanical modeling, we developed an artificial neural network as surrogate of the FE porcine brain model to predict the brain strain and the strain rate in a computationally efficient way. We assessed the effect of several experimental and modeling parameters on four FE-derived CC injury metrics (maximum principal strain, maximum principal strain rate, product of maximum principal strain and strain rate, and maximum shear strain). Next, we compared the in silico brain mechanical response with cortical damage data from in vivo CCI experiments on pig brains to evaluate the predictive performance of the CC injury metrics. Our ML surrogate was capable of rapidly predicting the outcome of the FE porcine brain undergoing CCI. The now computationally efficient MCS showed that depth and velocity of indentation were the most influential parameters for the strain and the strain rate-based injury metrics, respectively. The sensitivity analysis and comparison with the cortical damage experimental data indicate a better performance of maximum principal strain and maximum shear strain as tissue-level injury metrics for CC. These results provide guidelines to optimize the design of CCI tests and bring new insights to the understanding of the mechanical response of brain tissue to focal traumatic brain injury. Our findings also highlight the potential of using ML for computationally efficient TBI biomechanics investigations.
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Affiliation(s)
- Andrea Menichetti
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | - Laura Bartsoen
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | | | - Jos Vander Sloten
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | - Nele Famaey
- Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
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10
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van Osta N, Kirkels FP, van Loon T, Koopsen T, Lyon A, Meiburg R, Huberts W, Cramer MJ, Delhaas T, Haugaa KH, Teske AJ, Lumens J. Uncertainty Quantification of Regional Cardiac Tissue Properties in Arrhythmogenic Cardiomyopathy Using Adaptive Multiple Importance Sampling. Front Physiol 2021; 12:738926. [PMID: 34658923 PMCID: PMC8514656 DOI: 10.3389/fphys.2021.738926] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 08/31/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: Computational models of the cardiovascular system are widely used to simulate cardiac (dys)function. Personalization of such models for patient-specific simulation of cardiac function remains challenging. Measurement uncertainty affects accuracy of parameter estimations. In this study, we present a methodology for patient-specific estimation and uncertainty quantification of parameters in the closed-loop CircAdapt model of the human heart and circulation using echocardiographic deformation imaging. Based on patient-specific estimated parameters we aim to reveal the mechanical substrate underlying deformation abnormalities in patients with arrhythmogenic cardiomyopathy (AC). Methods: We used adaptive multiple importance sampling to estimate the posterior distribution of regional myocardial tissue properties. This methodology is implemented in the CircAdapt cardiovascular modeling platform and applied to estimate active and passive tissue properties underlying regional deformation patterns, left ventricular volumes, and right ventricular diameter. First, we tested the accuracy of this method and its inter- and intraobserver variability using nine datasets obtained in AC patients. Second, we tested the trueness of the estimation using nine in silico generated virtual patient datasets representative for various stages of AC. Finally, we applied this method to two longitudinal series of echocardiograms of two pathogenic mutation carriers without established myocardial disease at baseline. Results: Tissue characteristics of virtual patients were accurately estimated with a highest density interval containing the true parameter value of 9% (95% CI [0-79]). Variances of estimated posterior distributions in patient data and virtual data were comparable, supporting the reliability of the patient estimations. Estimations were highly reproducible with an overlap in posterior distributions of 89.9% (95% CI [60.1-95.9]). Clinically measured deformation, ejection fraction, and end-diastolic volume were accurately simulated. In presence of worsening of deformation over time, estimated tissue properties also revealed functional deterioration. Conclusion: This method facilitates patient-specific simulation-based estimation of regional ventricular tissue properties from non-invasive imaging data, taking into account both measurement and model uncertainties. Two proof-of-principle case studies suggested that this cardiac digital twin technology enables quantitative monitoring of AC disease progression in early stages of disease.
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Affiliation(s)
- Nick van Osta
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Feddo P Kirkels
- Division Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tim van Loon
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Tijmen Koopsen
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Aurore Lyon
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Roel Meiburg
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Wouter Huberts
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Maarten J Cramer
- Division Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tammo Delhaas
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Kristina H Haugaa
- Department of Cardiology, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Arco J Teske
- Division Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
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Romaszko L, Borowska A, Lazarus A, Dalton D, Berry C, Luo X, Husmeier D, Gao H. Neural network-based left ventricle geometry prediction from CMR images with application in biomechanics. Artif Intell Med 2021; 119:102140. [PMID: 34531009 DOI: 10.1016/j.artmed.2021.102140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 06/10/2021] [Accepted: 08/03/2021] [Indexed: 12/24/2022]
Abstract
Combining biomechanical modelling of left ventricular (LV) function and dysfunction with cardiac magnetic resonance (CMR) imaging has the potential to improve the prognosis of patient-specific cardiovascular disease risks. Biomechanical studies of LV function in three dimensions usually rely on a computerized representation of the LV geometry based on finite element discretization, which is essential for numerically simulating in vivo cardiac dynamics. Detailed knowledge of the LV geometry is also relevant for various other clinical applications, such as assessing the LV cavity volume and wall thickness. Accurately and automatically reconstructing personalized LV geometries from conventional CMR images with minimal manual intervention is still a challenging task, which is a pre-requisite for any subsequent automated biomechanical analysis. We propose a deep learning-based automatic pipeline for predicting the three-dimensional LV geometry directly from routinely-available CMR cine images, without the need to manually annotate the ventricular wall. Our framework takes advantage of a low-dimensional representation of the high-dimensional LV geometry based on principal component analysis. We analyze how the inference of myocardial passive stiffness is affected by using our automatically generated LV geometries instead of manually generated ones. These insights will inform the development of statistical emulators of LV dynamics to avoid computationally expensive biomechanical simulations. Our proposed framework enables accurate LV geometry reconstruction, outperforming previous approaches by delivering a reconstruction error 50% lower than reported in the literature. We further demonstrate that for a nonlinear cardiac mechanics model, using our reconstructed LV geometries instead of manually extracted ones only moderately affects the inference of passive myocardial stiffness described by an anisotropic hyperelastic constitutive law. The developed methodological framework has the potential to make an important step towards personalized medicine by eliminating the need for time consuming and costly manual operations. In addition, our method automatically maps the CMR scan into a low-dimensional representation of the LV geometry, which constitutes an important stepping stone towards the development of an LV geometry-heterogeneous emulator.
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Affiliation(s)
- Lukasz Romaszko
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Agnieszka Borowska
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Alan Lazarus
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - David Dalton
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Colin Berry
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Xiaoyu Luo
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Dirk Husmeier
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Hao Gao
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK.
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Cai L, Ren L, Wang Y, Xie W, Zhu G, Gao H. Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium. ROYAL SOCIETY OPEN SCIENCE 2021; 8:201121. [PMID: 33614068 PMCID: PMC7890479 DOI: 10.1098/rsos.201121] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 12/15/2020] [Indexed: 05/12/2023]
Abstract
A long-standing problem at the frontier of biomechanical studies is to develop fast methods capable of estimating material properties from clinical data. In this paper, we have studied three surrogate models based on machine learning (ML) methods for fast parameter estimation of left ventricular (LV) myocardium. We use three ML methods named K-nearest neighbour (KNN), XGBoost and multi-layer perceptron (MLP) to emulate the relationships between pressure and volume strains during the diastolic filling. Firstly, to train the surrogate models, a forward finite-element simulator of LV diastolic filling is used. Then the training data are projected in a low-dimensional parametrized space. Next, three ML models are trained to learn the relationships of pressure-volume and pressure-strain. Finally, an inverse parameter estimation problem is formulated by using those trained surrogate models. Our results show that the three ML models can learn the relationships of pressure-volume and pressure-strain very well, and the parameter inference using the surrogate models can be carried out in minutes. Estimated parameters from both the XGBoost and MLP models have much less uncertainties compared with the KNN model. Our results further suggest that the XGBoost model is better for predicting the LV diastolic dynamics and estimating passive parameters than other two surrogate models. Further studies are warranted to investigate how XGBoost can be used for emulating cardiac pump function in a multi-physics and multi-scale framework.
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Affiliation(s)
- Li Cai
- Xi’an Key Laboratory of Scientific Computation and Applied Statistics, Northwestern Polytechnical University, Xi’an 710129, China
- NPU-UoG International Cooperative Lab for Computation and Application in Cardiology, Northwestern Polytechnical University, Xi’an 710129, China
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710129, China
| | - Lei Ren
- Xi’an Key Laboratory of Scientific Computation and Applied Statistics, Northwestern Polytechnical University, Xi’an 710129, China
- NPU-UoG International Cooperative Lab for Computation and Application in Cardiology, Northwestern Polytechnical University, Xi’an 710129, China
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710129, China
| | - Yongheng Wang
- Xi’an Key Laboratory of Scientific Computation and Applied Statistics, Northwestern Polytechnical University, Xi’an 710129, China
- NPU-UoG International Cooperative Lab for Computation and Application in Cardiology, Northwestern Polytechnical University, Xi’an 710129, China
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710129, China
| | - Wenxian Xie
- Xi’an Key Laboratory of Scientific Computation and Applied Statistics, Northwestern Polytechnical University, Xi’an 710129, China
- NPU-UoG International Cooperative Lab for Computation and Application in Cardiology, Northwestern Polytechnical University, Xi’an 710129, China
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710129, China
| | - Guangyu Zhu
- School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Hao Gao
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QQ, UK
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