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van Veldhuizen WA, Schuurmann RCL, Zuidema R, Geraedts ACM, IJpma FFA, Kropman RHJ, Antoniou GA, van Sambeek MRHM, Balm R, Wolterink JM, de Vries JPPM. A Statistical Shape Model of Infrarenal Aortic Necks in Patients With and Without Late Type Ia Endoleak After Endovascular Aneurysm Repair. J Endovasc Ther 2024; 31:882-891. [PMID: 36647185 PMCID: PMC11402265 DOI: 10.1177/15266028221149913] [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] [Indexed: 01/18/2023]
Abstract
PURPOSE Hostile aortic neck characteristics, including short length, severe suprarenal and infrarenal angulation, conicity, and large diameter, have been associated with increased risk for type Ia endoleak (T1aEL) after endovascular aneurysm repair (EVAR). This study investigates the mid-term discriminative ability of a statistical shape model (SSM) of the infrarenal aortic neck morphology compared with or in combination with conventional measurements in patients who developed T1aEL post-EVAR. MATERIALS AND METHODS The dataset composed of EVAR patients who developed a T1aEL during follow-up and a control group without T1aEL. Principal component (PC) analysis was performed using a parametrization to create an SSM. Three logistic regression models were created. To discriminate between patients with and without T1aEL, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were calculated. RESULTS In total, 126 patients (84% male) were included. Median follow-up time in T1aEl group and control group was 52 (31, 78.5) and 51 (40, 62.5) months, respectively. Median follow-up time was not statistically different between the groups (p=0.72). A statistically significant difference between the median PC scores of the T1aEL and control groups was found for the first, eighth, and ninth PC. Sensitivity, specificity, and AUC values for the SSM-based versus the conventional measurements-based logistic regression models were 79%, 70%, and 0.82 versus 74%, 73%, and 0.85, respectively. The model of the SSM and conventional measurements combined resulted in sensitivity, specificity, and AUC of 81%, 81%, and 0.92. CONCLUSION An SSM of the infrarenal aortic neck determines its 3-dimensional geometry. The SSM is a potential valuable tool for risk stratification and T1aEL prediction in EVAR. The SSM complements the conventional measurements of the individual preoperative infrarenal aortic neck geometry by increasing the predictive value for late type Ia endoleak after standard EVAR. CLINICAL IMPACT A statistical shape model (SSM) determines the 3-dimensional geometry of the infrarenal aortic neck. The SSM complements the conventional measurements of the individual pre-operative infrarenal aortic neck geometry by increasing the predictive value for late type Ia endoleaks post-EVAR. The SSM is a potential valuable tool for risk stratification and late T1aEL prediction in EVAR and it is a first step toward implementation of a treatment planning support tool in daily clinical practice.
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Affiliation(s)
- Willemina A van Veldhuizen
- Department of Surgery, Division of Vascular Surgery, University Medical Center Groningen, Groningen, The Netherlands
| | - Richte C L Schuurmann
- Department of Surgery, Division of Vascular Surgery, University Medical Center Groningen, Groningen, The Netherlands
- Multi-Modality Medical Imaging (M3I) Group, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Roy Zuidema
- Department of Surgery, Division of Vascular Surgery, University Medical Center Groningen, Groningen, The Netherlands
| | - Anna C M Geraedts
- Department of Surgery, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Frank F A IJpma
- Department of Surgery, Division of Trauma Surgery, University Medical Center Groningen, Groningen, The Netherlands
| | - Rogier H J Kropman
- Department of Vascular Surgery, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - George A Antoniou
- Department of Vascular and Endovascular Surgery, Manchester University NHS Foundation Trust, Manchester, UK
- Division of Cardiovascular Sciences, School of Medical Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | | | - Ron Balm
- Department of Surgery, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Jelmer M Wolterink
- Department of Applied Mathematics, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Jean-Paul P M de Vries
- Department of Surgery, Division of Vascular Surgery, University Medical Center Groningen, Groningen, The Netherlands
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Chen J, Qian L, Ma L, Urakov T, Gu W, Liang L. SymTC: A symbiotic Transformer-CNN net for instance segmentation of lumbar spine MRI. Comput Biol Med 2024; 179:108795. [PMID: 38955128 DOI: 10.1016/j.compbiomed.2024.108795] [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: 02/10/2024] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 07/04/2024]
Abstract
Intervertebral disc disease, a prevalent ailment, frequently leads to intermittent or persistent low back pain, and diagnosing and assessing of this disease rely on accurate measurement of vertebral bone and intervertebral disc geometries from lumbar MR images. Deep neural network (DNN) models may assist clinicians with more efficient image segmentation of individual instances (discs and vertebrae) of the lumbar spine in an automated way, which is termed as instance image segmentation. In this work, we proposed SymTC, an innovative lumbar spine MR image segmentation model that combines the strengths of Transformer and Convolutional Neural Network (CNN). Specifically, we designed a parallel dual-path architecture to merge CNN layers and Transformer layers, and we integrated a novel position embedding into the self-attention module of Transformer, enhancing the utilization of positional information for more accurate segmentation. To further improve model performance, we introduced a new data synthesis technique to create synthetic yet realistic MR image dataset, named SSMSpine, which is made publicly available. We evaluated our SymTC and the other 16 representative image segmentation models on our private in-house dataset and public SSMSpine dataset, using two metrics, Dice Similarity Coefficient and the 95th percentile Hausdorff Distance. The results indicate that SymTC surpasses the other 16 methods, achieving the highest dice score of 96.169 % for segmenting vertebral bones and intervertebral discs on the SSMSpine dataset. The SymTC code and SSMSpine dataset are publicly available at https://github.com/jiasongchen/SymTC.
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Affiliation(s)
- Jiasong Chen
- Department of Computer Science, University of Miami, Coral Gables, FL, USA
| | - Linchen Qian
- Department of Computer Science, University of Miami, Coral Gables, FL, USA
| | - Linhai Ma
- Department of Computer Science, University of Miami, Coral Gables, FL, USA
| | - Timur Urakov
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Weiyong Gu
- Department of Mechanical and Aerospace Engineering, University of Miami, Coral Gables, FL, USA
| | - Liang Liang
- Department of Computer Science, University of Miami, Coral Gables, FL, USA.
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Mansouri H, Kemerli M, MacIver R, Amili O. Development of idealized human aortic models for in vitro and in silico hemodynamic studies. Front Cardiovasc Med 2024; 11:1358601. [PMID: 39161662 PMCID: PMC11330894 DOI: 10.3389/fcvm.2024.1358601] [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: 12/20/2023] [Accepted: 06/25/2024] [Indexed: 08/21/2024] Open
Abstract
Background The aorta, a central component of the cardiovascular system, plays a pivotal role in ensuring blood circulation. Despite its importance, there is a notable lack of idealized models for experimental and computational studies. Objective This study aims to develop computer-aided design (CAD) models for the idealized human aorta, intended for studying hemodynamics or solid mechanics in both in vitro and in silico settings. Methods Various parameters were extracted from comprehensive literature sources to evaluate major anatomical characteristics of the aorta in healthy adults, including variations in aortic arch branches and corresponding dimensions. The idealized models were generated based on averages weighted by the cohort size of each study for several morphological parameters collected and compiled from image-based or cadaveric studies, as well as data from four recruited subjects. The models were used for hemodynamics assessment using particle image velocimetry (PIV) measurements and computational fluid dynamics (CFD) simulations. Results Two CAD models for the idealized human aorta were developed, focusing on the healthy population. The CFD simulations, which align closely with the PIV measurements, capture the main global flow features and wall shear stress patterns observed in patient-specific cases, demonstrating the capabilities of the designed models. Conclusions The collected statistical data on the aorta and the two idealized aorta models, covering prevalent arch variants known as Normal and Bovine types, are shown to be useful for examining the hemodynamics of the aorta. They also hold promise for applications in designing medical devices where anatomical statistics are needed.
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Affiliation(s)
- Hamid Mansouri
- Department of Mechanical, Industrial, and Manufacturing Engineering, University of Toledo, Toledo, OH, United States
| | - Muaz Kemerli
- Department of Mechanical, Industrial, and Manufacturing Engineering, University of Toledo, Toledo, OH, United States
- Department of Mechanical Engineering, Sakarya University, Sakarya, Turkey
| | - Robroy MacIver
- Children’s Heart Clinic, Children’s Hospitals and Clinics of Minnesota, Minneapolis, MN, United States
| | - Omid Amili
- Department of Mechanical, Industrial, and Manufacturing Engineering, University of Toledo, Toledo, OH, United States
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van Veldhuizen WA, de Vries JPPM, Tuinstra A, Zuidema R, IJpma FFA, Wolterink JM, Schuurmann RCL. Machine Learning Based Prediction of Post-operative Infrarenal Endograft Apposition for Abdominal Aortic Aneurysms. Eur J Vasc Endovasc Surg 2024:S1078-5884(24)00567-7. [PMID: 38972630 DOI: 10.1016/j.ejvs.2024.07.003] [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: 10/05/2023] [Revised: 05/09/2024] [Accepted: 07/01/2024] [Indexed: 07/09/2024]
Abstract
OBJECTIVE Challenging infrarenal aortic neck characteristics have been associated with an increased risk of type Ia endoleak after endovascular aneurysm repair (EVAR). Short apposition (< 10 mm circumferential shortest apposition length [SAL]) on the first post-operative computed tomography angiography (CTA) has been associated with type Ia endoleak. Therefore, this study aimed to develop a model to predict post-operative SAL in patients with an abdominal aortic aneurysm based on the pre-operative shape. METHODS A statistical shape model was developed to obtain principal component scores. The dataset comprised patients treated by standard EVAR without complications (n = 93) enriched with patients with a late type Ia endoleak (n = 54). The infrarenal SAL was obtained from the first post-operative CTA and subsequently binarised (< 10 mm and ≥ 10 mm). The principal component scores that were statistically different between the SAL groups were used as input for five classification models, and evaluated by means of leave one out cross validation. Area under the receiver operating characteristic curves (AUC), accuracy, sensitivity, and specificity were determined for each classification model. RESULTS Of the 147 patients, 24 patients had an infrarenal SAL < 10 mm and 123 patients had a SAL ≥ 10 mm. The gradient boosting model resulted in the highest AUC of 0.77. Using this model, 114 patients (77.6%) were correctly classified; sensitivity (< 10 mm apposition was correctly predicted) and specificity (≥ 10 mm apposition was correctly predicted) were 0.70 and 0.79 based on a threshold of 0.21, respectively. CONCLUSION A model was developed to predict which patients undergoing EVAR will achieve sufficient graft apposition (≥ 10 mm) in the infrarenal aortic neck based on a statistical shape model of pre-operative CTA data. This model can help vascular specialists during the planning phase to accurately identify patients who are unlikely to achieve sufficient apposition after standard EVAR.
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Affiliation(s)
- Willemina A van Veldhuizen
- Department of Surgery, Division of Vascular Surgery, University Medical Centre Groningen, Groningen, The Netherlands.
| | - Jean-Paul P M de Vries
- Department of Surgery, Division of Vascular Surgery, University Medical Centre Groningen, Groningen, The Netherlands
| | - Annemarij Tuinstra
- Department of Surgery, Division of Vascular Surgery, University Medical Centre Groningen, Groningen, The Netherlands
| | - Roy Zuidema
- Department of Surgery, Division of Vascular Surgery, University Medical Centre Groningen, Groningen, The Netherlands
| | - Frank F A IJpma
- Department of Surgery, Division of Trauma Surgery, University Medical Centre Groningen, Groningen, The Netherlands
| | - Jelmer M Wolterink
- Department of Applied Mathematics, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Richte C L Schuurmann
- Department of Surgery, Division of Vascular Surgery, University Medical Centre Groningen, Groningen, The Netherlands; Multimodality Medical Imaging Group, Technical Medical Centre, University of Twente, Enschede, The Netherlands
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Czerny M, Grabenwöger M, Berger T, Aboyans V, Della Corte A, Chen EP, Desai ND, Dumfarth J, Elefteriades JA, Etz CD, Kim KM, Kreibich M, Lescan M, Di Marco L, Martens A, Mestres CA, Milojevic M, Nienaber CA, Piffaretti G, Preventza O, Quintana E, Rylski B, Schlett CL, Schoenhoff F, Trimarchi S, Tsagakis K, Siepe M, Estrera AL, Bavaria JE, Pacini D, Okita Y, Evangelista A, Harrington KB, Kachroo P, Hughes GC. EACTS/STS Guidelines for Diagnosing and Treating Acute and Chronic Syndromes of the Aortic Organ. Ann Thorac Surg 2024; 118:5-115. [PMID: 38416090 DOI: 10.1016/j.athoracsur.2024.01.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Affiliation(s)
- Martin Czerny
- Clinic for Cardiovascular Surgery, Department University Heart Center Freiburg Bad Krozingen, University Clinic Freiburg, Freiburg, Germany; Faculty of Medicine, Albert Ludwigs University Freiburg, Freiburg, Germany.
| | - Martin Grabenwöger
- Department of Cardiovascular Surgery, Clinic Floridsdorf, Vienna, Austria; Medical Faculty, Sigmund Freud Private University, Vienna, Austria.
| | - Tim Berger
- Clinic for Cardiovascular Surgery, Department University Heart Center Freiburg Bad Krozingen, University Clinic Freiburg, Freiburg, Germany; Faculty of Medicine, Albert Ludwigs University Freiburg, Freiburg, Germany
| | - Victor Aboyans
- Department of Cardiology, Dupuytren-2 University Hospital, Limoges, France; EpiMaCT, Inserm 1094 & IRD 270, Limoges University, Limoges, France
| | - Alessandro Della Corte
- Department of Translational Medical Sciences, University of Campania "L. Vanvitelli", Naples, Italy; Cardiac Surgery Unit, Monaldi Hospital, Naples, Italy
| | - Edward P Chen
- Division of Cardiovascular and Thoracic Surgery, Duke University Medical Center, Durham, North Carolina
| | - Nimesh D Desai
- Division of Cardiovascular Surgery, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Julia Dumfarth
- University Clinic for Cardiac Surgery, Medical University Innsbruck, Innsbruck, Austria
| | - John A Elefteriades
- Aortic Institute at Yale New Haven Hospital, Yale University School of Medicine, New Haven, Connecticut
| | - Christian D Etz
- Department of Cardiac Surgery, University Medicine Rostock, University of Rostock, Rostock, Germany
| | - Karen M Kim
- Division of Cardiovascular and Thoracic Surgery, The University of Texas at Austin/Dell Medical School, Austin, Texas
| | - Maximilian Kreibich
- Clinic for Cardiovascular Surgery, Department University Heart Center Freiburg Bad Krozingen, University Clinic Freiburg, Freiburg, Germany; Faculty of Medicine, Albert Ludwigs University Freiburg, Freiburg, Germany
| | - Mario Lescan
- Department of Thoracic and Cardiovascular Surgery, University Medical Centre Tübingen, Tübingen, Germany
| | - Luca Di Marco
- Cardiac Surgery Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Andreas Martens
- Department of Cardiac Surgery, Klinikum Oldenburg, Oldenburg, Germany; The Carl von Ossietzky University Oldenburg, Oldenburg, Germany
| | - Carlos A Mestres
- Department of Cardiothoracic Surgery and the Robert WM Frater Cardiovascular Research Centre, The University of the Free State, Bloemfontein, South Africa
| | - Milan Milojevic
- Department of Cardiac Surgery and Cardiovascular Research, Dedinje Cardiovascular Institute, Belgrade, Serbia
| | - Christoph A Nienaber
- Division of Cardiology at the Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom; National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Gabriele Piffaretti
- Vascular Surgery Department of Medicine and Surgery, University of Insubria School of Medicine, Varese, Italy
| | - Ourania Preventza
- Division of Cardiothoracic Surgery, Department of Surgery, University of Virginia, Charlottesville, Virginia
| | - Eduard Quintana
- Department of Cardiovascular Surgery, Hospital Clinic de Barcelona, University of Barcelona, Barcelona, Spain
| | - Bartosz Rylski
- Clinic for Cardiovascular Surgery, Department University Heart Center Freiburg Bad Krozingen, University Clinic Freiburg, Freiburg, Germany; Faculty of Medicine, Albert Ludwigs University Freiburg, Freiburg, Germany
| | - Christopher L Schlett
- Faculty of Medicine, Albert Ludwigs University Freiburg, Freiburg, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Freiburg, Freiburg, Germany
| | - Florian Schoenhoff
- Department of Cardiac Surgery, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Santi Trimarchi
- Department of Cardiac Thoracic and Vascular Diseases, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Konstantinos Tsagakis
- Department of Thoracic and Cardiovascular Surgery, West German Heart and Vascular Center, University Medicine Essen, Essen, Germany
| | - Matthias Siepe
- EACTS Review Coordinator; Department of Cardiac Surgery, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Anthony L Estrera
- STS Review Coordinator; Department of Cardiothoracic and Vascular Surgery, McGovern Medical School at UTHealth Houston, Houston, Texas
| | - Joseph E Bavaria
- Department of Cardiovascular Surgery, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Davide Pacini
- Division of Cardiac Surgery, S. Orsola University Hospital, IRCCS Bologna, Bologna, Italy
| | - Yutaka Okita
- Cardio-Aortic Center, Takatsuki General Hospital, Osaka, Japan
| | - Arturo Evangelista
- Department of Cardiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain; Vall d'Hebron Institut de Recerca, Barcelona, Spain; Biomedical Research Networking Center on Cardiovascular Diseases, Instituto de Salud Carlos III, Madrid, Spain; Departament of Medicine, Universitat Autònoma de Barcelona, Bellaterra, Spain; Instituto del Corazón, Quirónsalud-Teknon, Barcelona, Spain
| | - Katherine B Harrington
- Department of Cardiothoracic Surgery, Baylor Scott and White The Heart Hospital, Plano, Texas
| | - Puja Kachroo
- Division of Cardiothoracic Surgery, Washington University School of Medicine, St Louis, Missouri
| | - G Chad Hughes
- Division of Cardiovascular and Thoracic Surgery, Department of Surgery, Duke University Medical Center, Duke University, Durham, North Carolina
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Yao T, Pajaziti E, Quail M, Schievano S, Steeden J, Muthurangu V. Image2Flow: A proof-of-concept hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data. PLoS Comput Biol 2024; 20:e1012231. [PMID: 38900817 PMCID: PMC11218942 DOI: 10.1371/journal.pcbi.1012231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 07/02/2024] [Accepted: 06/06/2024] [Indexed: 06/22/2024] Open
Abstract
Computational fluid dynamics (CFD) can be used for non-invasive evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and time-consuming simulation. This study aims to train a deep learning model to both generate patient-specific volume-meshes of the pulmonary artery from 3D cardiac MRI data and directly estimate CFD flow fields. This proof-of-concept study used 135 3D cardiac MRIs from both a public and private dataset. The pulmonary arteries in the MRIs were manually segmented and converted into volume-meshes. CFD simulations were performed on ground truth meshes and interpolated onto point-point correspondent meshes to create the ground truth dataset. The dataset was split 110/10/15 for training, validation, and testing. Image2Flow, a hybrid image and graph convolutional neural network, was trained to transform a pulmonary artery template to patient-specific anatomy and CFD values, taking a specific inlet velocity as an additional input. Image2Flow was evaluated in terms of segmentation, and the accuracy of predicted CFD was assessed using node-wise comparisons. In addition, the ability of Image2Flow to respond to increasing inlet velocities was also evaluated. Image2Flow achieved excellent segmentation accuracy with a median Dice score of 0.91 (IQR: 0.86-0.92). The median node-wise normalized absolute error for pressure and velocity magnitude was 11.75% (IQR: 9.60-15.30%) and 9.90% (IQR: 8.47-11.90), respectively. Image2Flow also showed an expected response to increased inlet velocities with increasing pressure and velocity values. This proof-of-concept study has shown that it is possible to simultaneously perform patient-specific volume-mesh based segmentation and pressure and flow field estimation using Image2Flow. Image2Flow completes segmentation and CFD in ~330ms, which is ~5000 times faster than manual methods, making it more feasible in a clinical environment.
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Affiliation(s)
- Tina Yao
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Endrit Pajaziti
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Michael Quail
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Silvia Schievano
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Jennifer Steeden
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Vivek Muthurangu
- Institute of Cardiovascular Science, University College London, London, United Kingdom
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Liu M, Dong H, Mazlout A, Wu Y, Kalyanasundaram A, Oshinski JN, Sun W, Elefteriades JA, Leshnower BG, Gleason RL. The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy. Comput Biol Med 2024; 170:108041. [PMID: 38330820 DOI: 10.1016/j.compbiomed.2024.108041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/28/2023] [Accepted: 01/26/2024] [Indexed: 02/10/2024]
Abstract
OBJECTIVE Currently, the long-term outcomes of uncomplicated type B aortic dissection (TBAD) patients managed with optimal medical therapy (OMT) remain poor. Aortic expansion is a major factor that determines patient long-term survival. The objective of this study was to investigate the association between anatomic shape features and (i) OMT outcome; (ii) aortic growth rate for TBAD patients initially treated with OMT. METHODS 108 CT images of TBAD in the acute and chronic phases were collected from 46 patients who were initially treated with OMT. Statistical shape models (SSM) of TBAD were constructed to extract shape features from the earliest initial CT scans of each patient by using principal component analysis (PCA) and partial least square (PLS) regression. Additionally, conventional shape features (e.g., aortic diameter) were quantified from the earliest CT scans as a baseline for comparison. We identified conventional and SSM features that were significant in separating OMT "success" and failure patients. Moreover, the aortic growth rate was predicted by SSM and conventional features using linear and nonlinear regression with cross-validations. RESULTS Size-related SSM and conventional features (mean aortic diameter: p=0.0484, centerline length: p=0.0112, PCA score c1: p=0.0192, and PLS scores t1: p=0.0004, t2: p=0.0274) were significantly different between OMT success and failure groups, but these features were incapable of predicting the aortic growth rate. SSM shape features showed superior results in growth rate prediction compared to conventional features. Using multiple linear regression, the conventional, PCA, and PLS shape features resulted in root mean square errors (RMSE) of 1.23, 0.85, and 0.84 mm/year, respectively, in leave-one-out cross-validations. Nonlinear support vector regression (SVR) led to improved RMSE of 0.99, 0.54, and 0.43 mm/year, for the conventional, PCA, and PLS features, respectively. CONCLUSION Size-related shape features of the earliest scan were correlated with OMT failure but led to large errors in the prediction of the aortic growth rate. SSM features in combination with nonlinear regression could be a promising avenue to predict the aortic growth rate.
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Affiliation(s)
- Minliang Liu
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA; Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, USA
| | - Hai Dong
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA; Division of Cardiothoracic Surgery, Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Adam Mazlout
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Yuxuan Wu
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Asanish Kalyanasundaram
- Aortic Institute at Yale-New Haven Hospital, Yale University School of Medicine, New Haven, CT, USA
| | - John N Oshinski
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA; Department of Radiology & Imaging Science, Emory University, Atlanta, GA, USA
| | - Wei Sun
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - John A Elefteriades
- Aortic Institute at Yale-New Haven Hospital, Yale University School of Medicine, New Haven, CT, USA
| | - Bradley G Leshnower
- Division of Cardiothoracic Surgery, Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Rudolph L Gleason
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA; The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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8
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Khabaz K, Yuan K, Pugar J, Jiang D, Sankary S, Dhara S, Kim J, Kang J, Nguyen N, Cao K, Washburn N, Bohr N, Lee CJ, Kindlmann G, Milner R, Pocivavsek L. The geometric evolution of aortic dissections: Predicting surgical success using fluctuations in integrated Gaussian curvature. PLoS Comput Biol 2024; 20:e1011815. [PMID: 38306397 PMCID: PMC10866512 DOI: 10.1371/journal.pcbi.1011815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/14/2024] [Accepted: 01/09/2024] [Indexed: 02/04/2024] Open
Abstract
Clinical imaging modalities are a mainstay of modern disease management, but the full utilization of imaging-based data remains elusive. Aortic disease is defined by anatomic scalars quantifying aortic size, even though aortic disease progression initiates complex shape changes. We present an imaging-based geometric descriptor, inspired by fundamental ideas from topology and soft-matter physics that captures dynamic shape evolution. The aorta is reduced to a two-dimensional mathematical surface in space whose geometry is fully characterized by the local principal curvatures. Disease causes deviation from the smooth bent cylindrical shape of normal aortas, leading to a family of highly heterogeneous surfaces of varying shapes and sizes. To deconvolute changes in shape from size, the shape is characterized using integrated Gaussian curvature or total curvature. The fluctuation in total curvature (δK) across aortic surfaces captures heterogeneous morphologic evolution by characterizing local shape changes. We discover that aortic morphology evolves with a power-law defined behavior with rapidly increasing δK forming the hallmark of aortic disease. Divergent δK is seen for highly diseased aortas indicative of impending topologic catastrophe or aortic rupture. We also show that aortic size (surface area or enclosed aortic volume) scales as a generalized cylinder for all shapes. Classification accuracy for predicting aortic disease state (normal, diseased with successful surgery, and diseased with failed surgical outcomes) is 92.8±1.7%. The analysis of δK can be applied on any three-dimensional geometric structure and thus may be extended to other clinical problems of characterizing disease through captured anatomic changes.
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Affiliation(s)
- Kameel Khabaz
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Karen Yuan
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Joseph Pugar
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
- Departments of Material Science and Engineering, Biomedical Engineering, and Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - David Jiang
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Seth Sankary
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Sanjeev Dhara
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Junsung Kim
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Janet Kang
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Nhung Nguyen
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Kathleen Cao
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Newell Washburn
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Nicole Bohr
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Cheong Jun Lee
- Department of Surgery, NorthShore University Health System, Evanston, Illinois, United States of America
| | - Gordon Kindlmann
- Department of Computer Science, The University of Chicago, Chicago, Illinois, United States of America
| | - Ross Milner
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
| | - Luka Pocivavsek
- Department of Surgery, The University of Chicago, Chicago, Illinois, United States of America
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9
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Czerny M, Grabenwöger M, Berger T, Aboyans V, Della Corte A, Chen EP, Desai ND, Dumfarth J, Elefteriades JA, Etz CD, Kim KM, Kreibich M, Lescan M, Di Marco L, Martens A, Mestres CA, Milojevic M, Nienaber CA, Piffaretti G, Preventza O, Quintana E, Rylski B, Schlett CL, Schoenhoff F, Trimarchi S, Tsagakis K. EACTS/STS Guidelines for diagnosing and treating acute and chronic syndromes of the aortic organ. Eur J Cardiothorac Surg 2024; 65:ezad426. [PMID: 38408364 DOI: 10.1093/ejcts/ezad426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 09/15/2023] [Accepted: 12/19/2023] [Indexed: 02/28/2024] Open
Affiliation(s)
- Martin Czerny
- Clinic for Cardiovascular Surgery, Department University Heart Center Freiburg Bad Krozingen, University Clinic Freiburg, Freiburg, Germany
- Faculty of Medicine, Albert Ludwigs University Freiburg, Freiburg, Germany
| | - Martin Grabenwöger
- Department of Cardiovascular Surgery, Clinic Floridsdorf, Vienna, Austria
- Medical Faculty, Sigmund Freud Private University, Vienna, Austria
| | - Tim Berger
- Clinic for Cardiovascular Surgery, Department University Heart Center Freiburg Bad Krozingen, University Clinic Freiburg, Freiburg, Germany
- Faculty of Medicine, Albert Ludwigs University Freiburg, Freiburg, Germany
| | - Victor Aboyans
- Department of Cardiology, Dupuytren-2 University Hospital, Limoges, France
- EpiMaCT, Inserm 1094 & IRD 270, Limoges University, Limoges, France
| | - Alessandro Della Corte
- Department of Translational Medical Sciences, University of Campania "L. Vanvitelli", Naples, Italy
- Cardiac Surgery Unit, Monaldi Hospital, Naples, Italy
| | - Edward P Chen
- Division of Cardiovascular and Thoracic Surgery, Duke University Medical Center, Durham, NC, USA
| | - Nimesh D Desai
- Division of Cardiovascular Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia Dumfarth
- University Clinic for Cardiac Surgery, Medical University Innsbruck, Innsbruck, Austria
| | - John A Elefteriades
- Aortic Institute at Yale New Haven Hospital, Yale University School of Medicine, New Haven, CT, USA
| | - Christian D Etz
- Department of Cardiac Surgery, University Medicine Rostock, University of Rostock, Rostock, Germany
| | - Karen M Kim
- Division of Cardiovascular and Thoracic Surgery, The University of Texas at Austin/Dell Medical School, Austin, TX, USA
| | - Maximilian Kreibich
- Clinic for Cardiovascular Surgery, Department University Heart Center Freiburg Bad Krozingen, University Clinic Freiburg, Freiburg, Germany
- Faculty of Medicine, Albert Ludwigs University Freiburg, Freiburg, Germany
| | - Mario Lescan
- Department of Thoracic and Cardiovascular Surgery, University Medical Centre Tübingen, Tübingen, Germany
| | - Luca Di Marco
- Cardiac Surgery Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Andreas Martens
- Department of Cardiac Surgery, Klinikum Oldenburg, Oldenburg, Germany
- The Carl von Ossietzky University Oldenburg, Oldenburg, Germany
| | - Carlos A Mestres
- Department of Cardiothoracic Surgery and the Robert WM Frater Cardiovascular Research Centre, The University of the Free State, Bloemfontein, South Africa
| | - Milan Milojevic
- Department of Cardiac Surgery and Cardiovascular Research, Dedinje Cardiovascular Institute, Belgrade, Serbia
| | - Christoph A Nienaber
- Division of Cardiology at the Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, UK
| | - Gabriele Piffaretti
- Vascular Surgery Department of Medicine and Surgery, University of Insubria School of Medicine, Varese, Italy
| | - Ourania Preventza
- Division of Cardiothoracic Surgery, Department of Surgery, University of Virginia, Charlottesville, VA, USA
| | - Eduard Quintana
- Department of Cardiovascular Surgery, Hospital Clinic de Barcelona, University of Barcelona, Barcelona, Spain
| | - Bartosz Rylski
- Clinic for Cardiovascular Surgery, Department University Heart Center Freiburg Bad Krozingen, University Clinic Freiburg, Freiburg, Germany
- Faculty of Medicine, Albert Ludwigs University Freiburg, Freiburg, Germany
| | - Christopher L Schlett
- Faculty of Medicine, Albert Ludwigs University Freiburg, Freiburg, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Freiburg, Freiburg, Germany
| | - Florian Schoenhoff
- Department of Cardiac Surgery, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Santi Trimarchi
- Department of Cardiac Thoracic and Vascular Diseases, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Konstantinos Tsagakis
- Department of Thoracic and Cardiovascular Surgery, West German Heart and Vascular Center, University Medicine Essen, Essen, Germany
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10
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MacRaild M, Sarrami-Foroushani A, Lassila T, Frangi AF. Accelerated simulation methodologies for computational vascular flow modelling. J R Soc Interface 2024; 21:20230565. [PMID: 38350616 PMCID: PMC10864099 DOI: 10.1098/rsif.2023.0565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/12/2024] [Indexed: 02/15/2024] Open
Abstract
Vascular flow modelling can improve our understanding of vascular pathologies and aid in developing safe and effective medical devices. Vascular flow models typically involve solving the nonlinear Navier-Stokes equations in complex anatomies and using physiological boundary conditions, often presenting a multi-physics and multi-scale computational problem to be solved. This leads to highly complex and expensive models that require excessive computational time. This review explores accelerated simulation methodologies, specifically focusing on computational vascular flow modelling. We review reduced order modelling (ROM) techniques like zero-/one-dimensional and modal decomposition-based ROMs and machine learning (ML) methods including ML-augmented ROMs, ML-based ROMs and physics-informed ML models. We discuss the applicability of each method to vascular flow acceleration and the effectiveness of the method in addressing domain-specific challenges. When available, we provide statistics on accuracy and speed-up factors for various applications related to vascular flow simulation acceleration. Our findings indicate that each type of model has strengths and limitations depending on the context. To accelerate real-world vascular flow problems, we propose future research on developing multi-scale acceleration methods capable of handling the significant geometric variability inherent to such problems.
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Affiliation(s)
- Michael MacRaild
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Leeds, Leeds, UK
- EPSRC Centre for Doctoral Training in Fluid Dynamics, University of Leeds, Leeds, UK
| | - Ali Sarrami-Foroushani
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Leeds, Leeds, UK
- School of Health Science, University of Manchester, Manchester, UK
| | - Toni Lassila
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Leeds, Leeds, UK
- School of Computing, University of Leeds, Leeds, UK
| | - Alejandro F. Frangi
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Leeds, Leeds, UK
- School of Computer Science, University of Manchester, Manchester, UK
- School of Health Science, University of Manchester, Manchester, UK
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
- Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
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11
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Fu H, Zhang M, Leng J, Hu W, Zhu T, Zhang Y. Predicting Two-Photon Absorption Spectra of Octupolar Molecules: A Deep-Learning Approach Based Exclusively on Molecular Structures. J Phys Chem A 2024; 128:431-438. [PMID: 38190616 DOI: 10.1021/acs.jpca.3c07324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Octupolar molecules possessing a strong two-photon response are vital for numerous advanced applications. However, accurately predicting their two-photon absorption (TPA) spectra requires high-precision quantum chemical calculations, which are computationally expensive due to repeated simulations of molecular excited-state properties. To address this challenge, we introduce a deep learning approach capable of rapidly and accurately forecasting TPA spectra for octupolar molecules. By leveraging the geometric structure as an initial descriptor, we employ a graph neural network to predict the maximum two-photon transition wavelength and cross-section. Our model demonstrates a mean absolute percentage error of less than 4% compared to time-dependent density-functional theory calculations, effectively reproducing experimental observations. Notably, this deep learning technique is nearly 100 000 times faster than comparable quantum calculations, making it an efficient and cost-effective tool for simulating TPA properties of octupolar molecules. Furthermore, this method holds great promise for the high-throughput screening of exceptional TPA materials.
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Affiliation(s)
- Haoqing Fu
- International School for Optoelectronic Engineering, School of Chemistry and Chemical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Mengna Zhang
- International School for Optoelectronic Engineering, School of Chemistry and Chemical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Jiancai Leng
- International School for Optoelectronic Engineering, School of Chemistry and Chemical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Wei Hu
- International School for Optoelectronic Engineering, School of Chemistry and Chemical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Tong Zhu
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
- Institute for Advanced algorithms research, Shanghai 201306, China
| | - Yujin Zhang
- International School for Optoelectronic Engineering, School of Chemistry and Chemical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
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12
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Habibi MA, Fakhfouri A, Mirjani MS, Razavi A, Mortezaei A, Soleimani Y, Lotfi S, Arabi S, Heidaresfahani L, Sadeghi S, Minaee P, Eazi S, Rashidi F, Shafizadeh M, Majidi S. Prediction of cerebral aneurysm rupture risk by machine learning algorithms: a systematic review and meta-analysis of 18,670 participants. Neurosurg Rev 2024; 47:34. [PMID: 38183490 DOI: 10.1007/s10143-023-02271-2] [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: 11/15/2023] [Revised: 12/08/2023] [Accepted: 12/29/2023] [Indexed: 01/08/2024]
Abstract
It is possible to identify unruptured intracranial aneurysms (UIA) using machine learning (ML) algorithms, which can be a life-saving strategy, especially in high-risk populations. To better understand the importance and effectiveness of ML algorithms in practice, a systematic review and meta-analysis were conducted to predict cerebral aneurysm rupture risk. PubMed, Scopus, Web of Science, and Embase were searched without restrictions until March 20, 2023. Eligibility criteria included studies that used ML approaches in patients with cerebral aneurysms confirmed by DSA, CTA, or MRI. Out of 35 studies included, 33 were cohort, and 11 used digital subtraction angiography (DSA) as their reference imaging modality. Middle cerebral artery (MCA) and anterior cerebral artery (ACA) were the commonest locations of aneurysmal vascular involvement-51% and 40%, respectively. The aneurysm morphology was saccular in 48% of studies. Ten of 37 studies (27%) used deep learning techniques such as CNNs and ANNs. Meta-analysis was performed on 17 studies: sensitivity of 0.83 (95% confidence interval (CI), 0.77-0.88); specificity of 0.83 (95% CI, 0.75-0.88); positive DLR of 4.81 (95% CI, 3.29-7.02) and the negative DLR of 0.20 (95% CI, 0.14-0.29); a diagnostic score of 3.17 (95% CI, 2.55-3.78); odds ratio of 23.69 (95% CI, 12.75-44.01). ML algorithms can effectively predict the risk of rupture in cerebral aneurysms with good levels of accuracy, sensitivity, and specificity. However, further research is needed to enhance their diagnostic performance in predicting the rupture status of IA.
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Affiliation(s)
- Mohammad Amin Habibi
- Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Science, Tehran, Iran.
| | - Amirata Fakhfouri
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Mohammad Sina Mirjani
- Student Research Committee, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran
| | - Alireza Razavi
- Student Research Committee, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Ali Mortezaei
- Student Research Committee, Gonabad University of Medical Sciences, Gonabad, Iran
| | - Yasna Soleimani
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Sohrab Lotfi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Shayan Arabi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Ladan Heidaresfahani
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Sara Sadeghi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Poriya Minaee
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - SeyedMohammad Eazi
- School of Medicine, Islamic Azad University, Tehran Medical Sciences, Tehran, Iran
| | - Farhang Rashidi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Milad Shafizadeh
- Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Science, Tehran, Iran
| | - Shahram Majidi
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, USA
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13
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Liang L, Liu M, Elefteriades J, Sun W. Synergistic Integration of Deep Neural Networks and Finite Element Method with Applications of Nonlinear Large Deformation Biomechanics. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2023; 416:116347. [PMID: 38370344 PMCID: PMC10871671 DOI: 10.1016/j.cma.2023.116347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Patient-specific finite element analysis (FEA) holds great promise in advancing the prognosis of cardiovascular diseases by providing detailed biomechanical insights such as high-fidelity stress and deformation on a patient-specific basis. Albeit feasible, FEA that incorporates three-dimensional, complex patient-specific geometry can be time-consuming and unsuitable for time-sensitive clinical applications. To mitigate this challenge, machine learning (ML) models, e.g., deep neural networks (DNNs), have been increasingly utilized as potential alternatives to finite element method (FEM) for biomechanical analysis. So far, efforts have been made in two main directions: (1) learning the input-to-output mapping of traditional FEM solvers and replacing FEM with data-driven ML surrogate models; (2) solving equilibrium equations using physics-informed loss functions of neural networks. While these two existing strategies have shown improved performance in terms of speed or scalability, ML models have not yet provided practical advantages over traditional FEM due to generalization issues. This has led us to the question: instead of abandoning or replacing the traditional FEM framework that can reliably solve biomechanical problems, can we integrate FEM and DNNs to enhance performance? In this study, we propose a synergistic integration of DNNs and FEM to overcome their individual limitations. Using biomechanical analysis of the human aorta as the test bed, we demonstrated two novel integrative strategies in forward and inverse problems. For the forward problem, we developed DNNs with state-of-the-art architectures to predict a nodal displacement field, and this initial DNN solution was then updated by a FEM-based refinement process, yielding a fast and accurate computing framework. For the inverse problem of heterogeneous material parameter identification, our method employs DNN as a regularizer of the spatial distribution of material parameters, aiding the optimizer in locating the optimal solution. In our demonstrative examples, despite that the DNN-only forward models yielded small displacement errors in most test cases; stress errors were considerably large, and for some test cases, the peak stress errors were greater than 50%. Our DNN-FEM integration eliminated these non-negligible errors in DNN-only models and was magnitudes faster than the FEM-only approach. Additionally, compared to FEM-only inverse method with errors greater than 50%, our DNN-FEM inverse approach significantly improved the parameter identification accuracy and reduced the errors to less than 1%.
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Affiliation(s)
- Liang Liang
- Department of Computer Science, University of Miami, Coral Gables, FL
| | - Minliang Liu
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA
| | - John Elefteriades
- Aortic Institute, School of Medicine, Yale University, New Haven, CT
| | - Wei Sun
- Sutra Medical Inc, Lake Forest, CA
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14
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Wiputra H, Matsumoto S, Wagenseil JE, Braverman AC, Voeller RK, Barocas VH. Statistical shape representation of the thoracic aorta: accounting for major branches of the aortic arch. Comput Methods Biomech Biomed Engin 2023; 26:1557-1571. [PMID: 36165506 PMCID: PMC10040462 DOI: 10.1080/10255842.2022.2128672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 08/24/2022] [Accepted: 09/11/2022] [Indexed: 11/03/2022]
Abstract
Statistical shape modeling (SSM) is an emerging tool for risk assessment of thoracic aortic aneurysm. However, the head branches of the aortic arch are often excluded in SSM. We introduced an SSM strategy based on principal component analysis that accounts for aortic branches and applied it to a set of patient scans. Computational fluid dynamics were performed on the reconstructed geometries to identify the extent to which branch model accuracy affects the calculated wall shear stress (WSS) and pressure. Surface-averaged and location-specific values of pressure did not change significantly, but local WSS error was high near branches when inaccurately modeled.
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Affiliation(s)
- Hadi Wiputra
- Department of Biomedical Engineering, University of Minnesota
| | - Shion Matsumoto
- Department of Biomedical Engineering, University of Michigan
| | | | - Alan C. Braverman
- Department of Medicine, Cardiovascular Division, Washington University School of Medicine
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15
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Salmasi MY, Pirola S, Asimakopoulos G, Nienaber C, Athanasiou T. Risk prediction for thoracic aortic dissection: Is it time to go with the flow? J Thorac Cardiovasc Surg 2023; 166:1034-1042. [PMID: 35672182 DOI: 10.1016/j.jtcvs.2022.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 04/21/2022] [Accepted: 05/03/2022] [Indexed: 11/26/2022]
Affiliation(s)
- M Yousuf Salmasi
- Department of Surgery, Imperial College London, London, United Kingdom.
| | - Selene Pirola
- BHF Centre of Research Excellence, Institute of Clinical Sciences, Imperial College London, London, United Kingdom
| | - George Asimakopoulos
- Department of Cardiology, Royal Brompton and Harefield Trust, London, United Kingdom
| | - Christoph Nienaber
- Department of Cardiology, Royal Brompton and Harefield Trust, London, United Kingdom
| | - Thanos Athanasiou
- Department of Surgery, Imperial College London, London, United Kingdom
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16
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Huang K, Zhang J. Three-dimensional lumbar spine generation using variational autoencoder. Med Eng Phys 2023; 120:104046. [PMID: 37838400 DOI: 10.1016/j.medengphy.2023.104046] [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: 01/08/2023] [Revised: 08/16/2023] [Accepted: 09/04/2023] [Indexed: 10/16/2023]
Abstract
The disease analysis of the lumbar spine often requires a large number of three-dimensional (3D) models. Currently, there is a lack of 3D model of the lumbar spine for research, especially for the diseases such as scoliosis where it is difficult to collect sufficient data in a short period of time. To solve this problem, we develop an end-to-end network based on 3D variational autoencoder for randomly generating 3D lumbar spine model. In this network, the dual path encoder structure is used to fit two individual variables, i.e., mean and variance. Spatial coordinate attention modules are added to the encoder to improve the learning ability of the network to the 3D spatial structure of the lumbar spine. To enhance the power of the network to reconstruct the lumbar spine, a regularization loss is added to constrain the distribution loss. Additionally, Gaussian noise layers are added to the decoder to improve the authenticity and diversity of generated model. The experiments were conducted on the data of the entire lumbar spine and the individual lumbar vertebra, respectively. The results showed that the voxel intersection over union was 0.588 and 0.684, the voxel Dice coefficient was 0.739 and 0.811, the average surface distance was 0.807 and 1.189, and the Hausdorff distance was 2.615 and 3.710, for the entire lumbar spine and individual lumbar vertebra, respectively. The developed approach is comparable to the most commonly used model generation method of statistical shape model (SSM) in both visual and objective indicators, while the developed approach does not require the landmarks that is needed in the SSM method. Therefore, this fully automatic method can be easily used for population-based modeling of the lumbar spine which has the potential to be a powerful clinical tool.
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Affiliation(s)
- Kun Huang
- School of Information Science and Engineering, Yunnan University, Kunming, China
| | - Junhua Zhang
- School of Information Science and Engineering, Yunnan University, Kunming, China.
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17
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Nikpasand M, Abbott RE, Kage CC, Singh S, Winkelstein BA, Barocas VH, Ellingson AM. Cervical facet capsular ligament mechanics: Estimations based on subject-specific anatomy and kinematics. JOR Spine 2023; 6:e1269. [PMID: 37780821 PMCID: PMC10540825 DOI: 10.1002/jsp2.1269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/30/2023] [Accepted: 06/02/2023] [Indexed: 10/03/2023] Open
Abstract
Background To understand the facet capsular ligament's (FCL) role in cervical spine mechanics, the interactions between the FCL and other spinal components must be examined. One approach is to develop a subject-specific finite element (FE) model of the lower cervical spine, simulating the motion segments and their components' behaviors under physiological loading conditions. This approach can be particularly attractive when a patient's anatomical and kinematic data are available. Methods We developed and demonstrated methodology to create 3D subject-specific models of the lower cervical spine, with a focus on facet capsular ligament biomechanics. Displacement-controlled boundary conditions were applied to the vertebrae using kinematics extracted from biplane videoradiography during planar head motions, including axial rotation, lateral bending, and flexion-extension. The FCL geometries were generated by fitting a surface over the estimated ligament-bone attachment regions. The fiber structure and material characteristics of the ligament tissue were extracted from available human cervical FCL data. The method was demonstrated by application to the cervical geometry and kinematics of a healthy 23-year-old female subject. Results FCL strain within the resulting subject-specific model were subsequently compared to models with generic: (1) geometry, (2) kinematics, and (3) material properties to assess the effect of model specificity. Asymmetry in both the kinematics and the anatomy led to asymmetry in strain fields, highlighting the importance of patient-specific models. We also found that the calculated strain field was largely independent of constitutive model and driven by vertebrae morphology and motion, but the stress field showed more constitutive-equation-dependence, as would be expected given the highly constrained motion of cervical FCLs. Conclusions The current study provides a methodology to create a subject-specific model of the cervical spine that can be used to investigate various clinical questions by coupling experimental kinematics with multiscale computational models.
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Affiliation(s)
- Maryam Nikpasand
- Department of Mechanical EngineeringUniversity of Minnesota—Twin CitiesMinneapolisMinnesotaUSA
| | - Rebecca E. Abbott
- Department of Rehabilitation MedicineUniversity of Minnesota—Twin CitiesMinneapolisMinnesotaUSA
| | - Craig C. Kage
- Department of Rehabilitation MedicineUniversity of Minnesota—Twin CitiesMinneapolisMinnesotaUSA
| | - Sagar Singh
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Beth A. Winkelstein
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Victor H. Barocas
- Department of Mechanical EngineeringUniversity of Minnesota—Twin CitiesMinneapolisMinnesotaUSA
- Department of Biomedical EngineeringUniversity of Minnesota—Twin CitiesMinneapolisMinnesotaUSA
| | - Arin M. Ellingson
- Department of Rehabilitation MedicineUniversity of Minnesota—Twin CitiesMinneapolisMinnesotaUSA
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18
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Zafar MA, Ziganshin BA, Li Y, Ostberg NP, Rizzo JA, Tranquilli M, Mukherjee SK, Elefteriades JA. "Big Data" Analyses Underlie Clinical Discoveries at the Aortic Institute. THE YALE JOURNAL OF BIOLOGY AND MEDICINE 2023; 96:427-440. [PMID: 37780996 PMCID: PMC10524815 DOI: 10.59249/lndz2964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
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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Affiliation(s)
- Mohammad A. Zafar
- Aortic Institute at Yale New Haven Hospital, Yale
University School of Medicine, New Haven, CT, USA
| | - Bulat A. Ziganshin
- Aortic Institute at Yale New Haven Hospital, Yale
University School of Medicine, New Haven, CT, USA
| | - Yupeng Li
- Department of Political Sciences and Economics, Rowan
University, Glassboro, NJ USA
| | - Nicolai P. Ostberg
- Aortic Institute at Yale New Haven Hospital, Yale
University School of Medicine, New Haven, CT, USA
- New York University Grossman School of Medicine, New
York, NY, USA
| | - John A. Rizzo
- Department of Economics and Department of Preventive
Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Maryann Tranquilli
- Aortic Institute at Yale New Haven Hospital, Yale
University School of Medicine, New Haven, CT, USA
| | - Sandip K. Mukherjee
- Aortic Institute at Yale New Haven Hospital, Yale
University School of Medicine, New Haven, CT, USA
| | - John A. Elefteriades
- Aortic Institute at Yale New Haven Hospital, Yale
University School of Medicine, New Haven, CT, USA
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Lum RTW, Wang X, Zhang M, Zhang X, Ho JYK, Chow SCY, Fujikawa T, Wong RHL. Emerging role of radiogenomics in genetically triggered thoracic aortic aneurysm and dissection: a narrative review. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:356. [PMID: 37675315 PMCID: PMC10477616 DOI: 10.21037/atm-22-6149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 03/17/2023] [Indexed: 09/08/2023]
Abstract
Background and Objective Thoracic aortic aneurysm and dissection (TAAD) and its complications are life-threatening conditions. Hypertension and atherosclerosis had all along been recognized as the predominant risk factors for the development of TAAD. However, it was increasingly reported that genetic factors, such as single nucleotide polymorphisms (SNPs), are playing an important role in the disease development. The development of next-generation sequencing (NGS) and the rapid growth in radiomics provide a promising new platform to evaluate genetically triggered thoracic aortic aneurysm and dissection (GTAAD) from a new angle. This review is to present an overview of currently available knowledge regarding the use of radiomics and radiogenomics in GTAAD. Methods We performed literature searches in PubMed, EMBASE and Cochrane database from 2012 to 2022 regarding the use of radiomics and radiogenomics in GTAAD. Key Content and Findings There were only 13 studies on radiomics and 4 studies on radiogenomics integration retrieved from the search and it signifies there is still a significant knowledge gap in this field of translational medicine. An overview of the current knowledge of GTAAD, the workflow and role of radiomics, the radiogenomics integration for GTAAD including its potential role in the development of polygenic scores, as well as the implications, challenges, and limitations of radiogenomics research were discussed. Conclusions In the contemporary era, radiogenomics has been emerging as a state-of-the art approach to establish statistical correlation with radiomics features with genomic information in diagnosis, risk modeling and prediction and treatment decision in TAAD.
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Affiliation(s)
- Ray Tak Wai Lum
- Division of Cardiothoracic Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
| | - Xin Wang
- Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
| | - Miaoru Zhang
- Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
| | - Xianrui Zhang
- Department of Biomedical Science, The City University of Hong Kong, Hong Kong, China
| | - Jacky Yan Kit Ho
- Division of Cardiothoracic Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
| | - Simon Chi Ying Chow
- Division of Cardiothoracic Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
| | - Takuya Fujikawa
- Division of Cardiothoracic Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
| | - Randolph Hung Leung Wong
- Division of Cardiothoracic Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
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20
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Liang L, Liu M, Elefteriades J, Sun W. PyTorch-FEA: Autograd-enabled finite element analysis methods with applications for biomechanical analysis of human aorta. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 238:107616. [PMID: 37230048 PMCID: PMC10330852 DOI: 10.1016/j.cmpb.2023.107616] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND AND OBJECTIVES Finite-element analysis (FEA) is widely used as a standard tool for stress and deformation analysis of solid structures, including human tissues and organs. For instance, FEA can be applied at a patient-specific level to assist in medical diagnosis and treatment planning, such as risk assessment of thoracic aortic aneurysm rupture/dissection. These FEA-based biomechanical assessments often involve both forward and inverse mechanics problems. Current commercial FEA software packages (e.g., Abaqus) and inverse methods exhibit performance issues in either accuracy or speed. METHODS In this study, we propose and develop a new library of FEA code and methods, named PyTorch-FEA, by taking advantage of autograd, an automatic differentiation mechanism in PyTorch. We develop a class of PyTorch-FEA functionalities to solve forward and inverse problems with improved loss functions, and we demonstrate the capability of PyTorch-FEA in a series of applications related to human aorta biomechanics. In one of the inverse methods, we combine PyTorch-FEA with deep neural networks (DNNs) to further improve performance. RESULTS We applied PyTorch-FEA in four fundamental applications for biomechanical analysis of human aorta. In the forward analysis, PyTorch-FEA achieved a significant reduction in computational time without compromising accuracy compared with Abaqus, a commercial FEA package. Compared to other inverse methods, inverse analysis with PyTorch-FEA achieves better performance in either accuracy or speed, or both if combined with DNNs. CONCLUSIONS We have presented PyTorch-FEA, a new library of FEA code and methods, representing a new approach to develop FEA methods to forward and inverse problems in solid mechanics. PyTorch-FEA eases the development of new inverse methods and enables a natural integration of FEA and DNNs, which will have numerous potential applications.
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Affiliation(s)
- Liang Liang
- Department of Computer Science, University of Miami, Coral Gables, FL, United States.
| | - Minliang Liu
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - John Elefteriades
- Aortic Institute, School of Medicine, Yale University, New Haven, CT, United States
| | - Wei Sun
- Sutra Medical Inc, Lake Forest, CA, United States
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21
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Geronzi L, Martinez A, Rochette M, Yan K, Bel-Brunon A, Haigron P, Escrig P, Tomasi J, Daniel M, Lalande A, Lin S, Marin-Castrillon DM, Bouchot O, Porterie J, Valentini PP, Biancolini ME. Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate. Comput Biol Med 2023; 162:107052. [PMID: 37263151 DOI: 10.1016/j.compbiomed.2023.107052] [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: 03/30/2023] [Revised: 04/27/2023] [Accepted: 05/20/2023] [Indexed: 06/03/2023]
Abstract
OBJECTIVE ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict the ascending aortic aneurysm growth. MATERIAL AND METHODS 70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified. RESULTS the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth. CONCLUSION global shape features might provide an important contribution for predicting the aneurysm growth.
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Affiliation(s)
- Leonardo Geronzi
- University of Rome Tor Vergata, Department of Enterprise Engineering "Mario Lucertini", Rome, Italy; Ansys France, Villeurbanne, France.
| | - Antonio Martinez
- University of Rome Tor Vergata, Department of Enterprise Engineering "Mario Lucertini", Rome, Italy; Ansys France, Villeurbanne, France
| | | | - Kexin Yan
- Ansys France, Villeurbanne, France; University of Lyon, INSA Lyon, CNRS, LaMCoS, UMR5259, 69621 Villeurbanne, France
| | - Aline Bel-Brunon
- University of Lyon, INSA Lyon, CNRS, LaMCoS, UMR5259, 69621 Villeurbanne, France
| | - Pascal Haigron
- University of Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, F-35000, Rennes, France
| | - Pierre Escrig
- University of Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, F-35000, Rennes, France
| | - Jacques Tomasi
- University of Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, F-35000, Rennes, France
| | - Morgan Daniel
- University of Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, F-35000, Rennes, France
| | - Alain Lalande
- ICMUB Laboratory, CNRS 6302, University of Burgundy, 21078 Dijon, France; Medical Imaging Department, University Hospital of Dijon, Dijon, France
| | - Siyu Lin
- ICMUB Laboratory, CNRS 6302, University of Burgundy, 21078 Dijon, France; Medical Imaging Department, University Hospital of Dijon, Dijon, France
| | - Diana Marcela Marin-Castrillon
- ICMUB Laboratory, CNRS 6302, University of Burgundy, 21078 Dijon, France; Medical Imaging Department, University Hospital of Dijon, Dijon, France
| | - Olivier Bouchot
- Department of Cardio-Vascular and Thoracic Surgery, University Hospital of Dijon, Dijon, France
| | - Jean Porterie
- Cardiac Surgery Department, Rangueil University Hospital, Toulouse, France
| | - Pier Paolo Valentini
- University of Rome Tor Vergata, Department of Enterprise Engineering "Mario Lucertini", Rome, Italy
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22
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Tahir AM, Mutlu O, Bensaali F, Ward R, Ghareeb AN, Helmy SMHA, Othman KT, Al-Hashemi MA, Abujalala S, Chowdhury MEH, Alnabti ARDMH, Yalcin HC. Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes. J Clin Med 2023; 12:4774. [PMID: 37510889 PMCID: PMC10381346 DOI: 10.3390/jcm12144774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/08/2023] [Accepted: 04/10/2023] [Indexed: 07/30/2023] Open
Abstract
Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid-solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.
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Affiliation(s)
- Anas M Tahir
- Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Biomedical Research Center, Qatar University, Doha 2713, Qatar
| | - Onur Mutlu
- Biomedical Research Center, Qatar University, Doha 2713, Qatar
| | - Faycal Bensaali
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Rabab Ward
- Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Abdel Naser Ghareeb
- Heart Hospital, Hamad Medical Corporation, Doha 3050, Qatar
- Faculty of Medicine, Al Azhar University, Cairo 11884, Egypt
| | - Sherif M H A Helmy
- Noninvasive Cardiology Section, Cardiology Department, Heart Hospital, Hamad Medical Corporation, Doha 3050, Qatar
| | | | - Mohammed A Al-Hashemi
- Noninvasive Cardiology Section, Cardiology Department, Heart Hospital, Hamad Medical Corporation, Doha 3050, Qatar
| | | | | | | | - Huseyin C Yalcin
- Biomedical Research Center, Qatar University, Doha 2713, Qatar
- Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha 2713, Qatar
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23
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de Vries JPPM, Zuidema R, Bicknell CD, Fisher R, Gargiulo M, Louis N, Oikonomou K, Pratesi G, Reijnen MMPJ, Valdivia AR, Riambau V, Saucy F. European Expert Opinion on Infrarenal Sealing Zone Definition and Management in Endovascular Aortic Repair Patients: A Delphi Consensus. J Endovasc Ther 2023; 30:449-460. [PMID: 35297713 PMCID: PMC10209498 DOI: 10.1177/15266028221082006] [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] [Indexed: 11/17/2022]
Abstract
PURPOSE The purpose of the study was to provide a consensus definition of the infrarenal sealing zone and develop an algorithm to determine when and if adjunctive procedure(s) or reintervention should be considered in managing patients undergoing endovascular aortic repair (EVAR) for infrarenal abdominal aortic aneurysm (AAA). METHODS A European Advisory Board (AB), made up of 11 vascular surgeons with expertise in EVAR for AAA, was assembled to share their opinion regarding the definition of preoperative and postoperative infrarenal sealing zone. Information on their current clinical practice and level of agreement on proposed reintervention paths was used to develop an algorithm. The process included 2 virtual meetings and 2 rounds of online surveys completed by the AB (Delphi method). Consensus was defined as reached when ≥ 8 of 11 (73%) respondents agreed or were neutral. RESULTS The AB reached complete consensus on definitions and measurement of the pre-EVAR target anticipated sealing zone (TASZ) and the post-EVAR real achieved sealing zone (RASZ), namely, the shortest length between the proximal and distal reference points as defined by the AB, in case of patients with challenging anatomies. Also, agreement was achieved on a list of 4 anatomic parameters and 3 prosthesis-/procedure-related parameters, considered to have the most significant impact on preoperative and postoperative sealing zones. Furthermore, the agreement was reached that in the presence of visible neck-related complications, both adjunctive procedure(s) and reintervention should be contemplated (100% consensus). In addition, adjunctive procedure(s) or reintervention can be considered in the following cases (% consensus): insufficient sealing zone on completion imaging (91%) or on the first postoperative computed tomography (CT) scan (91%), suboptimal sealing zone on completion imaging (73%) or postoperative CT scan (82%), and negative evolution of the actual sealing zone over time (91%), even in the absence of visible complications. CONCLUSIONS AB members agreed on definitions of the pre- and post-EVAR infrarenal sealing zone, as well as factors of influence. Furthermore, a clinical decision algorithm was proposed to determine the timing and necessity of adjunctive procedure(s) and reinterventions.
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Affiliation(s)
- Jean-Paul P. M. de Vries
- Department of Surgery, Division of Vascular Surgery, University Medical Centre Groningen, Groningen, The Netherlands
| | - Roy Zuidema
- Department of Surgery, Division of Vascular Surgery, University Medical Centre Groningen, Groningen, The Netherlands
| | - Colin D. Bicknell
- Department of Surgery and Cancer, Imperial College London and Imperial Vascular Unit, Imperial College Healthcare NHS Trust, London, UK
| | - Robert Fisher
- Department of Vascular Surgery, Liverpool University Hospitals NHS Foundation Trust, Liverpool, Merseyside, UK
| | - Mauro Gargiulo
- Vascular Surgery, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Nicolas Louis
- Chirurgie vasculaire, Clinique Les Franciscaines, Nîmes, France
| | - Kyriakos Oikonomou
- Department of Vascular Surgery, University Medical Center Regensburg, Regensburg, Germany
- Department of Vascular and Endovascular Surgery, Cardiovascular Surgery Clinic, University Hospital Frankfurt and Johann Wolfgang Goethe University Frankfurt, Frankfurt, Germany
| | - Giovanni Pratesi
- Clinic of Vascular and Endovascular Surgery, Ospedale Policlinico San Martino, Department of Integrated Surgical and Diagnostic Sciences, University of Genoa, Genoa, Italy
| | - Michel M. P. J. Reijnen
- Department of Vascular Surgery, Rijnstate Hospital, Arnhem, The Netherlands
- Multi-Modality Medical Imaging Group, University of Twente, Enschede, The Netherlands
| | - Andrés Reyes Valdivia
- Department of Vascular and Endovascular Surgery, Ramón y Cajal’s University Hospital, Madrid, Spain
| | - Vincent Riambau
- Vascular Surgery Division, CardioVascular Institute, Hospital Clinic, Barcelona, Spain
| | - François Saucy
- Service of Vascular Surgery, Etablissement Hospitalier de la Côte, Morges, Switzerland
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Saitta S, Maga L, Armour C, Votta E, O'Regan DP, Salmasi MY, Athanasiou T, Weinsaft JW, Xu XY, Pirola S, Redaelli A. Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 233:107468. [PMID: 36921465 DOI: 10.1016/j.cmpb.2023.107468] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 02/15/2023] [Accepted: 03/05/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoratic aortic aneurysms (ATAA). To accurately reproduce in vivo hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements, still makes researchers resort to idealized BCs. The aim of this study was to generate and thoroughly characterize a large dataset of synthetic 4D aortic velocity profiles sampled on a 2D cross-section along the ascending aorta with features similar to clinical cohorts of patients with ATAA. METHODS Time-resolved 3D phase contrast magnetic resonance (4D flow MRI) scans of 30 subjects with ATAA were processed through in-house code to extract anatomically consistent cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated. RESULTS Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors. CONCLUSIONS We built a data-driven generative model of 4D aortic inlet velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set.
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Affiliation(s)
- Simone Saitta
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Ludovica Maga
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Chemical Engineering, Imperial College London, London, UK
| | - Chloe Armour
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Emiliano Votta
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - M Yousuf Salmasi
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Jonathan W Weinsaft
- Department of Medicine (Cardiology), Weill Cornell College, New York, NY, USA
| | - Xiao Yun Xu
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Selene Pirola
- Department of Chemical Engineering, Imperial College London, London, UK; Department of BioMechanical Engineering, 3mE Faculty, Delft University of Technology, Delft, Netherlands.
| | - Alberto Redaelli
- Department of Information, Electronics and Bioengineering, Politecnico di Milano, Milan, Italy
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Liang L, Liu M, Elefteriades J, Sun W. Synergistic Integration of Deep Neural Networks and Finite Element Method with Applications for Biomechanical Analysis of Human Aorta. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.03.535423. [PMID: 37066215 PMCID: PMC10104001 DOI: 10.1101/2023.04.03.535423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Motivation: Patient-specific finite element analysis (FEA) has the potential to aid in the prognosis of cardiovascular diseases by providing accurate stress and deformation analysis in various scenarios. It is known that patient-specific FEA is time-consuming and unsuitable for time-sensitive clinical applications. To mitigate this challenge, machine learning (ML) techniques, including deep neural networks (DNNs), have been developed to construct fast FEA surrogates. However, due to the data-driven nature of these ML models, they may not generalize well on new data, leading to unacceptable errors. Methods We propose a synergistic integration of DNNs and finite element method (FEM) to overcome each other’s limitations. We demonstrated this novel integrative strategy in forward and inverse problems. For the forward problem, we developed DNNs using state-of-the-art architectures, and DNN outputs were then refined by FEM to ensure accuracy. For the inverse problem of heterogeneous material parameter identification, our method employs a DNN as regularization for the inverse analysis process to avoid erroneous material parameter distribution. Results We tested our methods on biomechanical analysis of the human aorta. For the forward problem, the DNN-only models yielded acceptable stress errors in majority of test cases; yet, for some test cases that could be out of the training distribution (OOD), the peak stress errors were larger than 50%. The DNN-FEM integration eliminated the large errors for these OOD cases. Moreover, the DNN-FEM integration was magnitudes faster than the FEM-only approach. For the inverse problem, the FEM-only inverse method led to errors larger than 50%, and our DNN-FEM integration significantly improved performance on the inverse problem with errors less than 1%.
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26
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Liang L, Liu M, Elefteriades J, Sun W. PyTorch-FEA: Autograd-enabled Finite Element Analysis Methods with Applications for Biomechanical Analysis of Human Aorta. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.27.533816. [PMID: 37034587 PMCID: PMC10081215 DOI: 10.1101/2023.03.27.533816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Motivation Finite-element analysis (FEA) is widely used as a standard tool for stress and deformation analysis of solid structures, including human tissues and organs. For instance, FEA can be applied at a patient-specific level to assist in medical diagnosis and treatment planning, such as risk assessment of thoracic aortic aneurysm rupture/dissection. These FEA-based biomechanical assessments often involve both forward and inverse mechanics problems. Current commercial FEA software packages (e.g., Abaqus) and inverse methods exhibit performance issues in either accuracy or speed. Methods In this study, we propose and develop a new library of FEA code and methods, named PyTorch-FEA, by taking advantage of autograd, an automatic differentiation mechanism in PyTorch. We develop a class of PyTorch-FEA functionalities to solve forward and inverse problems with improved loss functions, and we demonstrate the capability of PyTorch-FEA in a series of applications related to human aorta biomechanics. In one of the inverse methods, we combine PyTorch-FEA with deep neural networks (DNNs) to further improve performance. Results We applied PyTorch-FEA in four fundamental applications for biomechanical analysis of human aorta. In the forward analysis, PyTorch-FEA achieved a significant reduction in computational time without compromising accuracy compared with Abaqus, a commercial FEA package. Compared to other inverse methods, inverse analysis with PyTorch-FEA achieves better performance in either accuracy or speed, or both if combined with DNNs.
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27
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Geronzi L, Haigron P, Martinez A, Yan K, Rochette M, Bel-Brunon A, Porterie J, Lin S, Marin-Castrillon DM, Lalande A, Bouchot O, Daniel M, Escrig P, Tomasi J, Valentini PP, Biancolini ME. Assessment of shape-based features ability to predict the ascending aortic aneurysm growth. Front Physiol 2023; 14:1125931. [PMID: 36950300 PMCID: PMC10025384 DOI: 10.3389/fphys.2023.1125931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/24/2023] [Indexed: 03/08/2023] Open
Abstract
The current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter D, are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from D, these are the ratio DCR between D and the length of the ascending aorta centerline, the ratio EILR between the length of the external and the internal lines and the tortuosity T. 50 patients with two 3D acquisitions at least 6 months apart were segmented and the growth rate (GR) with the shape features related to the first exam computed. The correlation between them has been investigated. After, the dataset was divided into two classes according to the growth rate value. We used six different classifiers with input data exclusively from the first exam to predict the class to which each patient belonged. A first classification was performed using only D and a second with all the shape features together. The performances have been evaluated by computing accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC) and positive (negative) likelihood ratio LHR+ (LHR-). A positive correlation was observed between growth rate and DCR (r = 0.511, p = 1.3e-4) and between GR and EILR (r = 0.472, p = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on D. Among the diameter-based classifiers, k-nearest neighbours (KNN) reported the best accuracy (86%), sensitivity (55.6%), AUROC (0.74), LHR+ (7.62) and LHR- (0.48). Concerning the classifiers based on the four shape features, we obtained the best accuracy (94%), sensitivity (66.7%), specificity (100%), AUROC (0.94), LHR+ (+∞) and LHR- (0.33) with support vector machine (SVM). This demonstrates how automatic shape features detection combined with risk classification criteria could be crucial in planning the follow-up of patients with ascending aortic aneurysm and in predicting the possible dangerous progression of the disease.
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Affiliation(s)
- Leonardo Geronzi
- Department of Enterprise Engineering “Mario Lucertini”, University of Rome Tor Vergata, Rome, Italy
- Ansys France, Villeurbanne, France
| | - Pascal Haigron
- LTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, France
| | - Antonio Martinez
- Department of Enterprise Engineering “Mario Lucertini”, University of Rome Tor Vergata, Rome, Italy
- Ansys France, Villeurbanne, France
| | - Kexin Yan
- Ansys France, Villeurbanne, France
- LaMCoS, Laboratoire de Mécanique des Contacts et des Structures, CNRS UMR5259, INSA Lyon, University of Lyon, Villeurbanne, France
| | | | - Aline Bel-Brunon
- LaMCoS, Laboratoire de Mécanique des Contacts et des Structures, CNRS UMR5259, INSA Lyon, University of Lyon, Villeurbanne, France
| | - Jean Porterie
- Cardiac Surgery Department, Rangueil University Hospital, Toulouse, France
| | - Siyu Lin
- IMVIA Laboratory, University of Burgundy, Dijon, France
- Medical Imaging Department, University Hospital of Dijon, Dijon, France
| | - Diana Marcela Marin-Castrillon
- IMVIA Laboratory, University of Burgundy, Dijon, France
- Medical Imaging Department, University Hospital of Dijon, Dijon, France
| | - Alain Lalande
- IMVIA Laboratory, University of Burgundy, Dijon, France
- Medical Imaging Department, University Hospital of Dijon, Dijon, France
| | - Olivier Bouchot
- Department of Cardio-Vascular and Thoracic Surgery, University Hospital of Dijon, Dijon, France
| | - Morgan Daniel
- LTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, France
| | - Pierre Escrig
- LTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, France
| | - Jacques Tomasi
- LTSI–UMR 1099, CHU Rennes, Inserm, University of Rennes, Rennes, France
| | - Pier Paolo Valentini
- Department of Enterprise Engineering “Mario Lucertini”, University of Rome Tor Vergata, Rome, Italy
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Williams JG, Marlevi D, Bruse JL, Nezami FR, Moradi H, Fortunato RN, Maiti S, Billaud M, Edelman ER, Gleason TG. Aortic Dissection is Determined by Specific Shape and Hemodynamic Interactions. Ann Biomed Eng 2022; 50:1771-1786. [PMID: 35943618 PMCID: PMC11262626 DOI: 10.1007/s10439-022-02979-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 05/11/2022] [Indexed: 12/30/2022]
Abstract
The aim of this study was to determine whether specific three-dimensional aortic shape features, extracted via statistical shape analysis (SSA), correlate with the development of thoracic ascending aortic dissection (TAAD) risk and associated aortic hemodynamics. Thirty-one patients followed prospectively with ascending thoracic aortic aneurysm (ATAA), who either did (12 patients) or did not (19 patients) develop TAAD, were included in the study, with aortic arch geometries extracted from computed tomographic angiography (CTA) imaging. Arch geometries were analyzed with SSA, and unsupervised and supervised (linked to dissection outcome) shape features were extracted with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), respectively. We determined PLS-DA to be effective at separating dissection and no-dissection patients ([Formula: see text]), with decreased tortuosity and more equal ascending and descending aortic diameters associated with higher dissection risk. In contrast, neither PCA nor traditional morphometric parameters (maximum diameter, tortuosity, or arch volume) were effective at separating dissection and no-dissection patients. The arch shapes associated with higher dissection probability were supported with hemodynamic insight. Computational fluid dynamics (CFD) simulations revealed a correlation between the PLS-DA shape features and wall shear stress (WSS), with higher maximum WSS in the ascending aorta associated with increased risk of dissection occurrence. Our work highlights the potential importance of incorporating higher dimensional geometric assessment of aortic arch anatomy in TAAD risk assessment, and in considering the interdependent influences of arch shape and hemodynamics as mechanistic contributors to TAAD occurrence.
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Affiliation(s)
- Jessica G Williams
- Thoracic and Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - David Marlevi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Jan L Bruse
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009, Donostia-San Sebastián, Spain
| | - Farhad R Nezami
- Thoracic and Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Hamed Moradi
- School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
| | - Ronald N Fortunato
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, USA
| | - Spandan Maiti
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Marie Billaud
- Thoracic and Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Thomas G Gleason
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA.
- University of Maryland School of Medicine, 110 S, Paca Street, 7th Floor, Baltimore, MD, 21201, USA.
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O'Rourke D, Surman TL, Abrahams JM, Edwards J, Reynolds KJ. Predicting rupture locations of ascending aortic aneurysms using CT-based finite element models. J Biomech 2022; 145:111351. [PMID: 36334320 DOI: 10.1016/j.jbiomech.2022.111351] [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: 10/11/2021] [Revised: 09/05/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022]
Abstract
Accurate rupture risk assessment of ascending aortic aneurysms is important for reducing aneurysm-related mortality. More recently, computational models have been shown to better predict rupture risk than diameter-based measurements. However, it remains unclear whether finite element (FE) models of the ascending aorta can predict rupture location, and over what timeframe those predictions are reliable. The aim of this study was to evaluate FE models of the ascending aorta generated from computed tomography (CT) scans in predicting rupture location. Pre- and post-rupture CT scans were obtained of 12 patients who underwent emergency surgical repair for ascending aorta rupture with varying time intervals between scans (20 days - 6 years). A rigid iterative closest point (ICP) registration was used to overlay post-rupture aortic geometries with pre-rupture FE models and directly compare predicted regions of high equivalent strain with actual rupture. The FE model predicted the rupture location in the 5 patients with the shortest time intervals between the pre- and post-rupture scans (20 days - 2 years, 3 months). However, rupture location was not predicted in the 4/5 patients with greater than 3 years between scans. Achieving a physiological equivalent strain distribution in the FE model was highly dependent on the resolution of the pre-rupture scan and whether contrast agent was present. The results suggest there may be a time interval beyond which FE predictions of rupture location may not be reliable. The results in this study may help clinical validation of FE models of ascending aortic aneurysms predicting rupture risk.
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Affiliation(s)
- Dermot O'Rourke
- Medical Device Research Institute, College of Science and Engineering, Flinders University. Australia.
| | - Timothy L Surman
- D'Arcy Sutherland Cardiothoracic Surgical Unit, Royal Adelaide Hospital, Adelaide, Australia
| | - John M Abrahams
- Centre for Orthopaedic and Trauma Research, The University of Adelaide, Adelaide, Australia
| | - James Edwards
- D'Arcy Sutherland Cardiothoracic Surgical Unit, Royal Adelaide Hospital, Adelaide, Australia
| | - Karen J Reynolds
- Medical Device Research Institute, College of Science and Engineering, Flinders University. Australia
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Anfinogenova ND, Sinitsyn VE, Kozlov BN, Panfilov DS, Popov SV, Vrublevsky AV, Chernyavsky A, Bergen T, Khovrin VV, Ussov WY. Existing and Emerging Approaches to Risk Assessment in Patients with Ascending Thoracic Aortic Dilatation. J Imaging 2022; 8:jimaging8100280. [PMID: 36286374 PMCID: PMC9605541 DOI: 10.3390/jimaging8100280] [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: 08/28/2022] [Revised: 09/20/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Ascending thoracic aortic aneurysm is a life-threatening disease, which is difficult to detect prior to the occurrence of a catastrophe. Epidemiology patterns of ascending thoracic aortic dilations/aneurysms remain understudied, whereas the risk assessment of it may be improved. The electronic databases PubMed/Medline 1966–2022, Web of Science 1975–2022, Scopus 1975–2022, and RSCI 1994–2022 were searched. The current guidelines recommend a purely aortic diameter-based assessment of the thoracic aortic aneurysm risk, but over 80% of the ascending aorta dissections occur at a size that is lower than the recommended threshold of 55 mm. Moreover, a 55 mm diameter criterion could exclude a vast majority (up to 99%) of the patients from preventive surgery. The authors review several visualization-based and alternative approaches which are proposed to better predict the risk of dissection in patients with borderline dilated thoracic aorta. The imaging-based assessments of the biomechanical aortic properties, the Young’s elastic modulus, the Windkessel function, compliance, distensibility, wall shear stress, pulse wave velocity, and some other parameters have been proposed to improve the risk assessment in patients with ascending thoracic aortic aneurysm. While the authors do not argue for shifting the diameter threshold to the left, they emphasize the need for more personalized solutions that integrate the imaging data with the patient’s genotypes and phenotypes in this heterogeneous pathology.
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Affiliation(s)
- Nina D. Anfinogenova
- Cardiology Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk 634012, Russia
- Correspondence: ; Tel.: +7-9095390220
| | | | - Boris N. Kozlov
- Cardiology Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk 634012, Russia
| | - Dmitry S. Panfilov
- Cardiology Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk 634012, Russia
| | - Sergey V. Popov
- Cardiology Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk 634012, Russia
| | - Alexander V. Vrublevsky
- Cardiology Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk 634012, Russia
| | | | - Tatyana Bergen
- E. Meshalkin National Medical Research Center, Novosibirsk 630055, Russia
| | - Valery V. Khovrin
- Petrovsky National Research Centre of Surgery, Moscow 119991, Russia
| | - Wladimir Yu. Ussov
- Cardiology Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk 634012, Russia
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31
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Shu Z, Chen S, Wang W, Qiu Y, Yu Y, Lyu N, Wang C. Machine Learning Algorithms for Rupture Risk Assessment of Intracranial Aneurysms: A Diagnostic Meta-Analysis. World Neurosurg 2022; 165:e137-e147. [PMID: 35690311 DOI: 10.1016/j.wneu.2022.05.117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 01/10/2023]
Abstract
OBJECTIVE Several machine learning algorithms have been increasingly applied to predict the rupture risk of intracranial aneurysms. We performed the present diagnostic meta-analysis to comprehensively evaluate the diagnostic value of machine learning algorithms for assessing the rupture risk of intracranial aneurysms. METHODS We systematically searched 3 electronic databases, including Medline (via PubMed), the Cochrane Register of Controlled Trials (via Ovid), and Embase (via Elsevier), to retrieve eligible studies from the databases' inception through March 2021. The latest update was performed in June 2021. StataMP, version 14, was used to estimate all pooled diagnostic values. RESULTS A total of 4 studies involving 6 reports were considered to meet the inclusion criteria. Our diagnostic meta-analysis generated the following pooled diagnostic values: sensitivity, 0.84 (95% confidence interval [CI], 0.75-0.90); specificity, 0.78 (95% CI, 0.68-0.85); positive likelihood ratio, 3.8 (95% CI, 2.4-5.9); negative likelihood ratio, 0.21 (95% CI, 0.12-0.35), diagnostic odd ratio, 18 (95% CI, 7-46), and area under the summary receiver operating characteristic curve, 0.88 (95% CI, 0.85-0.90). CONCLUSIONS Our findings have demonstrated that the diagnostic performance of machine learning algorithms for the rupture risk assessment of AIs is excellent. Considering that the negative effects resulted from the limited number of eligible studies, we suggest developing more well-designed studies with larger sample sizes to validate our findings.
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Affiliation(s)
- Zhang Shu
- Department of Neurosurgery, The First People's Hospital of Taicang, Taicang, China
| | - Song Chen
- Department of Neurosurgery, The First People's Hospital of Taicang, Taicang, China
| | - Wei Wang
- Department of Neurosurgery, The First People's Hospital of Taicang, Taicang, China
| | - Yufa Qiu
- Department of Neurosurgery, The First People's Hospital of Taicang, Taicang, China
| | - Ying Yu
- Department of Neurosurgery, Changhai Hospital of Shanghai, Shanghai, China
| | - Nan Lyu
- Department of Neurosurgery, Changhai Hospital of Shanghai, Shanghai, China
| | - Chi Wang
- Department of Neurosurgery, The First People's Hospital of Taicang, Taicang, China.
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32
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Li X, Liu X, Deng X, Fan Y. Interplay between Artificial Intelligence and Biomechanics Modeling in the Cardiovascular Disease Prediction. Biomedicines 2022; 10:2157. [PMID: 36140258 PMCID: PMC9495955 DOI: 10.3390/biomedicines10092157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/26/2022] [Accepted: 08/28/2022] [Indexed: 11/16/2022] Open
Abstract
Cardiovascular disease (CVD) is the most common cause of morbidity and mortality worldwide, and early accurate diagnosis is the key point for improving and optimizing the prognosis of CVD. Recent progress in artificial intelligence (AI), especially machine learning (ML) technology, makes it possible to predict CVD. In this review, we first briefly introduced the overview development of artificial intelligence. Then we summarized some ML applications in cardiovascular diseases, including ML-based models to directly predict CVD based on risk factors or medical imaging findings and the ML-based hemodynamics with vascular geometries, equations, and methods for indirect assessment of CVD. We also discussed case studies where ML could be used as the surrogate for computational fluid dynamics in data-driven models and physics-driven models. ML models could be a surrogate for computational fluid dynamics, accelerate the process of disease prediction, and reduce manual intervention. Lastly, we briefly summarized the research difficulties and prospected the future development of AI technology in cardiovascular diseases.
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Affiliation(s)
- Xiaoyin Li
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
| | - Xiao Liu
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
| | - Xiaoyan Deng
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
| | - Yubo Fan
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
- School of Engineering Medicine, Beihang University, Beijing 100083, China
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33
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Gholamalizadeh T, Moshfeghifar F, Ferguson Z, Schneider T, Panozzo D, Darkner S, Makaremi M, Chan F, Søndergaard PL, Erleben K. Open-Full-Jaw: An open-access dataset and pipeline for finite element models of human jaw. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 224:107009. [PMID: 35872385 DOI: 10.1016/j.cmpb.2022.107009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 07/02/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND State-of-the-art finite element studies on human jaws are mostly limited to the geometry of a single patient. In general, developing accurate patient-specific computational models of the human jaw acquired from cone-beam computed tomography (CBCT) scans is labor-intensive and non-trivial, which involves time-consuming human-in-the-loop procedures, such as segmentation, geometry reconstruction, and re-meshing tasks. Therefore, with the current practice, researchers need to spend considerable time and effort to produce finite element models (FEMs) to get to the point where they can use the models to answer clinically-interesting questions. Besides, any manual task involved in the process makes it difficult for the researchers to reproduce identical models generated in the literature. Hence, a quantitative comparison is not attainable due to the lack of surface/volumetric meshes and FEMs. METHODS We share an open-access repository composed of 17 patient-specific computational models of human jaws and the utilized pipeline for generating them for reproducibility of our work. The used pipeline minimizes the required time for processing and any potential biases in the model generation process caused by human intervention. It gets the segmented geometries with irregular and dense surface meshes and provides reduced, adaptive, watertight, and conformal surface/volumetric meshes, which can directly be used in finite element (FE) analysis. RESULTS We have quantified the variability of our 17 models and assessed the accuracy of the developed models from three different aspects; (1) the maximum deviations from the input meshes using the Hausdorff distance as an error measurement, (2) the quality of the developed volumetric meshes, and (3) the stability of the FE models under two different scenarios of tipping and biting. CONCLUSIONS The obtained results indicate that the developed computational models are precise, and they consist of quality meshes suitable for various FE scenarios. We believe the provided dataset of models including a high geometrical variation obtained from 17 different models will pave the way for population studies focusing on the biomechanical behavior of human jaws.
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Affiliation(s)
- Torkan Gholamalizadeh
- Department of Computer Science, University of Copenhagen, Copenhagen 2100, Denmark; 3Shape A/S, Copenhagen 1060, Denmark.
| | - Faezeh Moshfeghifar
- Department of Computer Science, University of Copenhagen, Copenhagen 2100, Denmark
| | - Zachary Ferguson
- Courant Institute of Mathematical Sciences, New York University, 60 5th Ave, New York NY 10011, USA
| | - Teseo Schneider
- Department of Computer Science, University of Victoria, Victoria BC V8P 5C2, Canada
| | - Daniele Panozzo
- Courant Institute of Mathematical Sciences, New York University, 60 5th Ave, New York NY 10011, USA
| | - Sune Darkner
- Department of Computer Science, University of Copenhagen, Copenhagen 2100, Denmark
| | - Masrour Makaremi
- Dentofacial Orthopedics Department, University of Bordeaux, Bordeaux, France; Orthodontie clinic, 2 Rue des 2 Conils, Bergerac 24100, France
| | - François Chan
- Orthodontie clinic, 2 Rue des 2 Conils, Bergerac 24100, France
| | | | - Kenny Erleben
- Department of Computer Science, University of Copenhagen, Copenhagen 2100, Denmark
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Rostam-Alilou AA, Safari M, Jarrah HR, Zolfagharian A, Bodaghi M. A machine learning model for non-invasive detection of atherosclerotic coronary artery aneurysm. Int J Comput Assist Radiol Surg 2022; 17:2221-2229. [PMID: 35948765 DOI: 10.1007/s11548-022-02725-w] [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: 01/28/2022] [Accepted: 07/19/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Atherosclerosis plays a significant role in the initiation of coronary artery aneurysms (CAA). Although the treatment options for this kind of vascular disease are developing, there are challenges and limitations in both selecting and applying sufficient medical solutions. For surgical interventions, that are novel therapies, non-invasive specific patient-based studies could lead to obtaining more promising results. Despite medical and pathological tests, these pre-surgical investigations require special biomedical and computer-aided engineering techniques. In this study, a machine learning (ML) model is proposed for the non-invasive detection of atherosclerotic CAA for the first time. METHODS The database for study was collected from hemodynamic analysis and computed tomography angiography (CTA) of 80 CAAs from 61 patients, approved by the Institutional Review Board (IRB). The proposed ML model is formulated for learning by a one-class support vector machine (1SVM) that is a field of ML to provide techniques for outlier and anomaly detection. RESULTS The applied ML algorithms yield reasonable results with high and significant accuracy in designing a procedure for the non-invasive diagnosis of atherosclerotic aneurysms. This proposed method could be employed as a unique artificial intelligence (AI) tool for assurance in clinical decision-making procedures for surgical intervention treatment methods in the future. CONCLUSIONS The non-invasive diagnosis of the atherosclerotic CAAs, which is one of the vital factors in the accomplishment of endovascular surgeries, is important due to some clinical decisions. Although there is no accurate tool for managing this kind of diagnosis, an ML model that can decrease the probability of endovascular surgical failures, death risk, and post-operational complications is proposed in this study. The model is able to increase the clinical decision accuracy for low-risk selection of treatment options.
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Affiliation(s)
- Ali A Rostam-Alilou
- Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, UK
| | - Marziyeh Safari
- Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, UK
| | - Hamid R Jarrah
- Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, UK
| | - Ali Zolfagharian
- School of Engineering, Deakin University, Geelong, 3216, Australia
| | - Mahdi Bodaghi
- Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, UK.
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Goswami S, Li DS, Rego BV, Latorre M, Humphrey JD, Karniadakis GE. Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms. J R Soc Interface 2022; 19:20220410. [PMID: 36043289 PMCID: PMC9428523 DOI: 10.1098/rsif.2022.0410] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/05/2022] [Indexed: 11/12/2022] Open
Abstract
Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta that can lead to life-threatening dissection or rupture. In vivo assessments of TAA progression are largely limited to measurements of aneurysm size and growth rate. There is promise, however, that computational modelling of the evolving biomechanics of the aorta could predict future geometry and properties from initiating mechanobiological insults. We present an integrated framework to train a deep operator network (DeepONet)-based surrogate model to identify TAA contributing factors using synthetic finite-element-based datasets. For training, we employ a constrained mixture model of aortic growth and remodelling to generate maps of local aortic dilatation and distensibility for multiple TAA risk factors. We evaluate the performance of the surrogate model for insult distributions varying from fusiform (analytically defined) to complex (randomly generated). We propose two frameworks, one trained on sparse information and one on full-field greyscale images, to gain insight into a preferred neural operator-based approach. We show that this continuous learning approach can predict the patient-specific insult profile associated with any given dilatation and distensibility map with high accuracy, particularly when based on full-field images. Our findings demonstrate the feasibility of applying DeepONet to support transfer learning of patient-specific inputs to predict TAA progression.
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Affiliation(s)
- Somdatta Goswami
- Division of Applied Mathematics, Brown University, Providence, RI, USA
| | - David S. Li
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Bruno V. Rego
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Marcos Latorre
- Centre for Research and Innovation in Bioengineering, Universitat Politècnica de València, València, Spain
| | - Jay D. Humphrey
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - George Em Karniadakis
- Division of Applied Mathematics, Brown University, Providence, RI, USA
- School of Engineering, Brown University, Providence, RI, USA
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36
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Bazzi MS, Balouchzadeh R, Pavey SN, Quirk JD, Yanagisawa H, Vedula V, Wagenseil JE, Barocas VH. Experimental and Mouse-Specific Computational Models of the Fbln4 SMKO Mouse to Identify Potential Biomarkers for Ascending Thoracic Aortic Aneurysm. Cardiovasc Eng Technol 2022; 13:558-572. [PMID: 35064559 PMCID: PMC9304450 DOI: 10.1007/s13239-021-00600-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 11/28/2021] [Indexed: 11/02/2022]
Abstract
PURPOSE To use computational methods to explore geometric, mechanical, and fluidic biomarkers that could correlate with mouse lifespan in the Fbln4SMKO mouse. Mouse lifespan was used as a surrogate for risk of a severe cardiovascular event in cases of ascending thoracic aortic aneurysm. METHODS Image-based, mouse-specific fluid-structure-interaction models were developed for Fbln4SMKO mice (n = 10) at ages two and six months. The results of the simulations were used to quantify potential biofluidic biomarkers, complementing the geometrical biomarkers obtained directly from the images. RESULTS Comparing the different geometrical and biofluidic biomarkers to the mouse lifespan, it was found that mean oscillatory shear index (OSImin) and minimum time-averaged wall shear stress (TAWSSmin) at six months showed the largest correlation with lifespan (r2 = 0.70, 0.56), with both correlations being positive (i.e., mice with high OSImean and high TAWSSmin tended to live longer). When change between two and six months was considered, the change in TAWSSmin showed a much stronger correlation than OSImean (r2 = 0.75 vs. 0.24), and the correlation was negative (i.e., mice with increasing TAWSSmin over this period tended to live less long). CONCLUSION The results highlight potential biomarkers of ATAA outcomes that can be obtained through noninvasive imaging and computational simulations, and they illustrate the potential synergy between small-animal and computational models.
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Affiliation(s)
- Marisa S Bazzi
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Ramin Balouchzadeh
- Department of Mechanical Engineering & Materials Science, Washington University, St. Louis, MO, 63110, USA
| | - Shawn N Pavey
- Department of Mechanical Engineering & Materials Science, Washington University, St. Louis, MO, 63110, USA
| | - James D Quirk
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Hiromi Yanagisawa
- Life Science Center for Survival Dynamics, Tsukuba Advanced Research Alliance, University of Tsukuba, Tsukuba, Japan
| | - Vijay Vedula
- Department of Mechanical Engineering, Columbia University, New York, NY, 10027, USA
| | - Jessica E Wagenseil
- Department of Mechanical Engineering & Materials Science, Washington University, St. Louis, MO, 63110, USA
| | - Victor H Barocas
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, 55455, USA.
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Goubergrits L, Vellguth K, Obermeier L, Schlief A, Tautz L, Bruening J, Lamecker H, Szengel A, Nemchyna O, Knosalla C, Kuehne T, Solowjowa N. CT-Based Analysis of Left Ventricular Hemodynamics Using Statistical Shape Modeling and Computational Fluid Dynamics. Front Cardiovasc Med 2022; 9:901902. [PMID: 35865389 PMCID: PMC9294248 DOI: 10.3389/fcvm.2022.901902] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/07/2022] [Indexed: 11/17/2022] Open
Abstract
Background Cardiac computed tomography (CCT) based computational fluid dynamics (CFD) allows to assess intracardiac flow features, which are hypothesized as an early predictor for heart diseases and may support treatment decisions. However, the understanding of intracardiac flow is challenging due to high variability in heart shapes and contractility. Using statistical shape modeling (SSM) in combination with CFD facilitates an intracardiac flow analysis. The aim of this study is to prove the usability of a new approach to describe various cohorts. Materials and Methods CCT data of 125 patients (mean age: 60.6 ± 10.0 years, 16.8% woman) were used to generate SSMs representing aneurysmatic and non-aneurysmatic left ventricles (LVs). Using SSMs, seven group-averaged LV shapes and contraction fields were generated: four representing patients with and without aneurysms and with mild or severe mitral regurgitation (MR), and three distinguishing aneurysmatic patients with true, intermediate aneurysms, and globally hypokinetic LVs. End-diastolic LV volumes of the groups varied between 258 and 347 ml, whereas ejection fractions varied between 21 and 26%. MR degrees varied from 1.0 to 2.5. Prescribed motion CFD was used to simulate intracardiac flow, which was analyzed regarding large-scale flow features, kinetic energy, washout, and pressure gradients. Results SSMs of aneurysmatic and non-aneurysmatic LVs were generated. Differences in shapes and contractility were found in the first three shape modes. Ninety percent of the cumulative shape variance is described with approximately 30 modes. A comparison of hemodynamics between all groups found shape-, contractility- and MR-dependent differences. Disturbed blood washout in the apex region was found in the aneurysmatic cases. With increasing MR, the diastolic jet becomes less coherent, whereas energy dissipation increases by decreasing kinetic energy. The poorest blood washout was found for the globally hypokinetic group, whereas the weakest blood washout in the apex region was found for the true aneurysm group. Conclusion The proposed CCT-based analysis of hemodynamics combining CFD with SSM seems promising to facilitate the analysis of intracardiac flow, thus increasing the value of CCT for diagnostic and treatment decisions. With further enhancement of the computational approach, the methodology has the potential to be embedded in clinical routine workflows and support clinicians.
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Affiliation(s)
- Leonid Goubergrits
- Institute of Computer-Assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
| | - Katharina Vellguth
- Institute of Computer-Assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lukas Obermeier
- Institute of Computer-Assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Adriano Schlief
- Institute of Computer-Assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lennart Tautz
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Jan Bruening
- Institute of Computer-Assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | | | | - Olena Nemchyna
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
| | - Christoph Knosalla
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Titus Kuehne
- Institute of Computer-Assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
| | - Natalia Solowjowa
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
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Adikari D, Gharleghi R, Zhang S, Jorm L, Sowmya A, Moses D, Ooi SY, Beier S. A new and automated risk prediction of coronary artery disease using clinical endpoints and medical imaging-derived patient-specific insights: protocol for the retrospective GeoCAD cohort study. BMJ Open 2022; 12:e054881. [PMID: 35725256 PMCID: PMC9214399 DOI: 10.1136/bmjopen-2021-054881] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
INTRODUCTION Coronary artery disease (CAD) is the leading cause of death worldwide. More than a quarter of cardiovascular events are unexplained by current absolute cardiovascular disease risk calculators, and individuals without clinical risk factors have been shown to have worse outcomes. The 'anatomy of risk' hypothesis recognises that adverse anatomical features of coronary arteries enhance atherogenic haemodynamics, which in turn mediate the localisation and progression of plaques. We propose a new risk prediction method predicated on CT coronary angiography (CTCA) data and state-of-the-art machine learning methods based on a better understanding of anatomical risk for CAD. This may open new pathways in the early implementation of personalised preventive therapies in susceptible individuals as a potential key in addressing the growing burden of CAD. METHODS AND ANALYSIS GeoCAD is a retrospective cohort study in 1000 adult patients who have undergone CTCA for investigation of suspected CAD. It is a proof-of-concept study to test the hypothesis that advanced image-derived patient-specific data can accurately predict long-term cardiovascular events. The objectives are to (1) profile CTCA images with respect to variations in anatomical shape and associated haemodynamic risk expressing, at least in part, an individual's CAD risk, (2) develop a machine-learning algorithm for the rapid assessment of anatomical risk directly from unprocessed CTCA images and (3) to build a novel CAD risk model combining traditional risk factors with these novel anatomical biomarkers to provide a higher accuracy CAD risk prediction tool. ETHICS AND DISSEMINATION The study protocol has been approved by the St Vincent's Hospital Human Research Ethics Committee, Sydney-2020/ETH02127 and the NSW Population and Health Service Research Ethics Committee-2021/ETH00990. The project outcomes will be published in peer-reviewed and biomedical journals, scientific conferences and as a higher degree research thesis.
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Affiliation(s)
- Dona Adikari
- Faculty of Medicine, The University of New South Wales, Sydney, New South Wales, Australia
- Cardiology Department, The Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Ramtin Gharleghi
- School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, New South Wales, Australia
| | - Shisheng Zhang
- School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, New South Wales, Australia
| | - Louisa Jorm
- Centre for Big Data Research in Health, The University of New South Wales, Sydney, New South Wales, Australia
| | - Arcot Sowmya
- School of Computer Science and Engineering, The University of New South Wales, Sydney, New South Wales, Australia
| | - Daniel Moses
- School of Computer Science and Engineering, The University of New South Wales, Sydney, New South Wales, Australia
- Department of Medical Imaging, The Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Sze-Yuan Ooi
- Faculty of Medicine, The University of New South Wales, Sydney, New South Wales, Australia
- Cardiology Department, The Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Susann Beier
- School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, New South Wales, Australia
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Multi-fidelity surrogate modeling through hybrid machine learning for biomechanical and finite element analysis of soft tissues. Comput Biol Med 2022; 148:105699. [PMID: 35715259 DOI: 10.1016/j.compbiomed.2022.105699] [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: 01/17/2022] [Revised: 05/01/2022] [Accepted: 06/04/2022] [Indexed: 11/22/2022]
Abstract
Biomechanical simulation enables medical researchers to study complex mechano-biological conditions, although for soft tissue modeling, it may apply highly nonlinear multi-physics theories commonly implemented by expensive finite element (FE) solvers. This is a significantly time-consuming process on a regular computer and completely inefficient in urgent situations. One remedy is to first generate a dataset of the possible inputs and outputs of the solver in order to then train an efficient machine learning (ML) model, i.e., the supervised ML-based surrogate, replacing the expensive solver to speed up the simulation. But it still requires a large number of expensive numerical samples. In this regard, we propose a hybrid ML (HML) method that uses a reduced-order model defined by the simplification of the complex multi-physics equations to produce a dataset of the low-fidelity (LF) results. The surrogate then has this efficient numerical model and an ML model that should increase the fidelity of its outputs to the level of high-fidelity (HF) results. Based on our empirical tests via a group of diverse training and numerical modeling conditions, the proposed method can improve training convergence for very limited training samples. In particular, while considerable time gains comparing to the HF numerical models are observed, training of the HML models is also significantly more efficient than the purely ML-based surrogates. From this, we conclude that this non-destructive HML implementation may increase the accuracy and efficiency of surrogate modeling of soft tissues with complex multi-physics properties in small data regimes.
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Abstract
Computational fluid dynamics (CFD) modeling of blood flow plays an important role in better understanding various medical conditions, designing more effective drug delivery systems, and developing novel diagnostic methods and treatments. However, despite significant advances in computational technology and resources, the expensive computational cost of these simulations still hinders their transformation from a research interest to a clinical tool. This bottleneck is even more severe for image-based, patient-specific CFD simulations with realistic boundary conditions and complex computational domains, which make such simulations excessively expensive. To address this issue, deep learning approaches have been recently explored to accelerate computational hemodynamics simulations. In this study, we review recent efforts to integrate deep learning with CFD and discuss the applications of this approach in solving hemodynamics problems, such as blood flow behavior in aorta and cerebral arteries. We also discuss potential future directions in the field. In this review, we suggest that incorporating physiologic understandings and underlying fluid mechanics laws in deep learning models will soon lead to a paradigm shift in the development novel non-invasive computational medical decisions.
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41
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Dong H, Liu M, Qin T, Liang L, Ziganshin B, Ellauzi H, Zafar M, Jang S, Elefteriades J, Sun W. Engineering analysis of aortic wall stress and root dilatation in the V-shape surgery for treatment of ascending aortic aneurysms. Interact Cardiovasc Thorac Surg 2022; 34:1124-1131. [PMID: 35134955 PMCID: PMC9159430 DOI: 10.1093/icvts/ivac004] [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: 09/08/2021] [Revised: 12/11/2021] [Accepted: 12/17/2021] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVES The study objective was to evaluate the aortic wall stress and root dilatation before and after the novel V-shape surgery for the treatment of ascending aortic aneurysms and root ectasia. METHODS Clinical cardiac computed tomography images were obtained for 14 patients [median age, 65 years (range, 33-78); 10 (71%) males] who underwent the V-shape surgery. For 10 of the 14 patients, the computed tomography images of the whole aorta pre- and post-surgery were available, and finite element simulations were performed to obtain the stress distributions of the aortic wall at pre- and post-surgery states. For 6 of the 14 patients, the computed tomography images of the aortic root were available at 2 follow-up time points post-surgery (Post 1, within 4 months after surgery and Post 2, about 20-52 months from Post 1). We analysed the root dilatation post-surgery using change of the effective diameter of the root at the two time points and investigated the relationship between root wall stress and root dilatation. RESULTS The mean and peak max-principal stresses of the aortic root exhibit a significant reduction, P=0.002 between pre- and post-surgery for both root mean stress (median among the 10 patients presurgery, 285.46 kPa; post-surgery, 199.46 kPa) and root peak stress (median presurgery, 466.66 kPa; post-surgery, 342.40 kPa). The mean and peak max-principal stresses of the ascending aorta also decrease significantly from pre- to post-surgery, with P=0.004 for the mean value (median presurgery, 296.48 kPa; post-surgery, 183.87 kPa), and P=0.002 for the peak value (median presurgery, 449.73 kPa; post-surgery, 282.89 kPa), respectively. The aortic root diameter after the surgery has an average dilatation of 5.01% in total and 2.15%/year. Larger root stress results in larger root dilatation. CONCLUSIONS This study marks the first biomechanical analysis of the novel V-shape surgery. The study has demonstrated significant reduction in wall stress of the aortic root repaired by the surgery. The root was able to dilate mildly post-surgery. Wall stress could be a critical factor for the dilatation since larger root stress results in larger root dilatation. The dilated aortic root within 4 years after surgery is still much smaller than that of presurgery.
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Affiliation(s)
- Hai Dong
- Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Minliang Liu
- Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Tongran Qin
- Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Liang Liang
- Department of Computer Science, University of Miami, Coral Gables, FL, USA
| | - Bulat Ziganshin
- Aortic Institute at Yale New Haven Hospital, Yale University School of Medicine, New Haven, CT, USA
| | - Hesham Ellauzi
- Aortic Institute at Yale New Haven Hospital, Yale University School of Medicine, New Haven, CT, USA
| | - Mohammad Zafar
- Aortic Institute at Yale New Haven Hospital, Yale University School of Medicine, New Haven, CT, USA
| | - Sophie Jang
- Aortic Institute at Yale New Haven Hospital, Yale University School of Medicine, New Haven, CT, USA
| | - John Elefteriades
- Aortic Institute at Yale New Haven Hospital, Yale University School of Medicine, New Haven, CT, USA
| | - Wei Sun
- Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Corresponding author. Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Technology Enterprise Park, Room 206 387 Technology Circle, Atlanta, GA 30313-2412, USA. Tel: (404)-385-1245; e-mail: (W. Sun)
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Sündermann SH, Hennemuth A, Kempfert J. Virtual reality in cardiac interventions-New tools or new toys? J Card Surg 2022; 37:2466-2468. [PMID: 35610730 DOI: 10.1111/jocs.16569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 04/18/2022] [Indexed: 11/30/2022]
Abstract
Improvementsin medical imaging and a steady increase in computing power are leading to new possibilities in the field of cardiovascular interventions. Interventions can be planned in advance in greater detail, even to the point of simulating procedures. Nevertheless, all techniques are at an early stage of development. It is of utmost importance that tools, especially if they can be used as decision support are intensively validated and their accuracy is demonstrated. In our commentary, we summarize current techniques for impprovements in planning and guiding of procedures, but also critically discuss the downsides of these techniques. Following the work of Kenichi and colleagues, we also discuss necessary steps in advancing new tools and techniques, particularly as they are used in routine clinical practice. We also discuss the role of artificial intelligence, which could play a crucial role in this context in the future.
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Affiliation(s)
- Simon H Sündermann
- Department of Cardiovascular Surgery, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany.,DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
| | - Anja Hennemuth
- Insitute of Computer-Assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jörg Kempfert
- Department of Cardiovascular Surgery, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany.,DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
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Nabaei M. Cerebral aneurysm evolution modeling from microstructural computational models to machine learning: A review. Comput Biol Chem 2022; 98:107676. [DOI: 10.1016/j.compbiolchem.2022.107676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 02/13/2022] [Accepted: 03/30/2022] [Indexed: 11/03/2022]
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A Statistical Shape Model of the Morphological Variation of the Infrarenal Abdominal Aortic Aneurysm Neck. J Clin Med 2022; 11:jcm11061687. [PMID: 35330011 PMCID: PMC8948978 DOI: 10.3390/jcm11061687] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/10/2022] [Accepted: 03/16/2022] [Indexed: 11/16/2022] Open
Abstract
Hostile aortic neck characteristics, such as short length and large diameter, have been associated with type Ia endoleaks and reintervention after endovascular aneurysm repair (EVAR). However, such characteristics partially describe the complex aortic neck morphology. A more comprehensive quantitative description of 3D neck shape might lead to new insights into the relationship between aortic neck morphology and EVAR outcomes in individual patients. This study identifies the 3D morphological shape components that describe the infrarenal aortic neck through a statistical shape model (SSM). Pre-EVAR CT scans of 97 patients were used to develop the SSM. Parameterization of the morphology was based on the center lumen line reconstruction, a triangular surface mesh of the aortic lumen, 3D coordinates of the renal arteries, and the distal end of the aortic neck. A principal component analysis of the parametrization of the aortic neck coordinates was used as input for the SSM. The SSM consisted of 96 principal components (PCs) that each described a unique shape feature. The first five PCs represented 95% of the total morphological variation in the dataset. The SSM is an objective model that provides a quantitative description of the neck morphology of an individual patient.
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Establishment of a Combined Diagnostic Model of Abdominal Aortic Aneurysm with Random Forest and Artificial Neural Network. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7173972. [PMID: 35299890 PMCID: PMC8922147 DOI: 10.1155/2022/7173972] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 01/22/2022] [Accepted: 02/17/2022] [Indexed: 11/17/2022]
Abstract
Objectives. Abdominal aortic aneurysm (AAA), a disease with high mortality, is limited by the current diagnostic methods in the early screening. This study aimed to screen novel and significant biomarkers and construct a diagnostic model for AAA by using a novel machine learning method, i.e., an ensemble of the random forest (RF) algorithm and artificial neural network (ANN). Methods and Results. Through a search of the Gene Expression Omnibus (GEO) database, two large-sample gene expression datasets (GSE57691 and GSE47472) were downloaded and preprocessed. Differentially expressed genes (DEGs) in GSE57691 were identified by R software, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Essential metabolic pathways related to positive regulation of cell death and NAD binding were found. Then, RF was used to identify key genes from the DEGs, and an AAA diagnostic model was established by ANN. A transcription factor (TF) regulatory network of key genes related to angiogenesis and endothelial migration was constructed. Finally, a validation dataset was used to validate the model and the area under the receiver operating characteristic curve (AUC) value was high. Conclusion. Potential AAA-associated gene biomarkers were identified by RF, and a novel early diagnostic model of AAA was established by ANN. The AUC indicated that the diagnostic model had a highly satisfactory diagnostic performance. In conclusion, this study will provide a promising theoretical basis for further clinical and experimental studies.
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46
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Dong H, Liu M, Qin T, Liang L, Ziganshin B, Ellauzi H, Zafar M, Jang S, Elefteriades J, Sun W, Gleason RL. A novel computational growth framework for biological tissues: Application to growth of aortic root aneurysm repaired by the V-shape surgery. J Mech Behav Biomed Mater 2022; 127:105081. [DOI: 10.1016/j.jmbbm.2022.105081] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/28/2021] [Accepted: 01/08/2022] [Indexed: 01/15/2023]
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47
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Nita CI, Puiu A, Bunescu D, Mihai Itu L, Mihalef V, Chintalapani G, Armstrong A, Zampi J, Benson L, Sharma P, Rapaka S. Personalized Pre- and Post-Operative Hemodynamic Assessment of Aortic Coarctation from 3D Rotational Angiography. Cardiovasc Eng Technol 2022; 13:14-40. [PMID: 34145556 DOI: 10.1007/s13239-021-00552-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 05/25/2021] [Indexed: 01/12/2023]
Abstract
PURPOSE Coarctation of Aorta (CoA) is a congenital disease consisting of a narrowing that obstructs the systemic blood flow. This proof-of-concept study aimed to develop a framework for automatically and robustly personalizing aortic hemodynamic computations for the assessment of pre- and post-intervention CoA patients from 3D rotational angiography (3DRA) data. METHODS We propose a framework that combines hemodynamic modelling and machine learning (ML) based techniques, and rely on 3DRA data for non-invasive pressure computation in CoA patients. The key features of our framework are a parameter estimation method for calibrating inlet and outlet boundary conditions, and regional mechanical wall properties, to ensure that the computational results match the patient-specific measurements, and an improved ML based pressure drop model capable of predicting the instantaneous pressure drop for a wide range of flow conditions and anatomical CoA variations. RESULTS We evaluated the framework by investigating 6 patient datasets, under pre- and post-operative setting, and, since all calibration procedures converged successfully, the proposed approach is deemed robust. We compared the peak-to-peak and the cycle-averaged pressure drop computed using the reduced-order hemodynamic model with the catheter based measurements, before and after virtual and actual stenting. The mean absolute error for the peak-to-peak pressure drop, which is the most relevant measure for clinical decision making, was 2.98 mmHg for the pre- and 2.11 mmHg for the post-operative setting. Moreover, the proposed method is computationally efficient: the average execution time was of only [Formula: see text] minutes on a standard hardware configuration. CONCLUSION The use of 3DRA for hemodynamic modelling could allow for a complete hemodynamic assessment, as well as virtual interventions or surgeries and predictive modeling. However, before such an approach can be used routinely, significant advancements are required for automating the workflow.
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Affiliation(s)
- Cosmin-Ioan Nita
- Advanta, Siemens SRL, 3A Eroilor, 500007, Brasov, Romania.,Automation and Information Technology, Transilvania University of Brasov, 5 Mihai Viteazu, 5000174, Brasov, Romania
| | - Andrei Puiu
- Advanta, Siemens SRL, 3A Eroilor, 500007, Brasov, Romania.,Automation and Information Technology, Transilvania University of Brasov, 5 Mihai Viteazu, 5000174, Brasov, Romania
| | - Daniel Bunescu
- Advanta, Siemens SRL, 3A Eroilor, 500007, Brasov, Romania.,Automation and Information Technology, Transilvania University of Brasov, 5 Mihai Viteazu, 5000174, Brasov, Romania
| | - Lucian Mihai Itu
- Advanta, Siemens SRL, 3A Eroilor, 500007, Brasov, Romania. .,Automation and Information Technology, Transilvania University of Brasov, 5 Mihai Viteazu, 5000174, Brasov, Romania.
| | - Viorel Mihalef
- Digital Services, Digital Technology & Innovation, Siemens Healthineers, 755 College Road, Princeton, NJ, 08540, USA
| | | | - Aimee Armstrong
- The Heart Center, Nationwide Children's Hospital, Columbus, OH, USA
| | - Jeffrey Zampi
- The Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Lee Benson
- The Division of Cardiology, The Labatt Family Heart Center, The Hospital for Sick Children, Toronto, Canada
| | - Puneet Sharma
- Digital Services, Digital Technology & Innovation, Siemens Healthineers, 755 College Road, Princeton, NJ, 08540, USA
| | - Saikiran Rapaka
- Digital Services, Digital Technology & Innovation, Siemens Healthineers, 755 College Road, Princeton, NJ, 08540, USA
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Yin M, Ban E, Rego BV, Zhang E, Cavinato C, Humphrey JD, Em Karniadakis G. Simulating progressive intramural damage leading to aortic dissection using DeepONet: an operator-regression neural network. J R Soc Interface 2022; 19:20210670. [PMID: 35135299 PMCID: PMC8826120 DOI: 10.1098/rsif.2021.0670] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 12/23/2021] [Indexed: 12/28/2022] Open
Abstract
Aortic dissection progresses mainly via delamination of the medial layer of the wall. Notwithstanding the complexity of this process, insight has been gleaned by studying in vitro and in silico the progression of dissection driven by quasi-static pressurization of the intramural space by fluid injection, which demonstrates that the differential propensity of dissection along the aorta can be affected by spatial distributions of structurally significant interlamellar struts that connect adjacent elastic lamellae. In particular, diverse histological microstructures may lead to differential mechanical behaviour during dissection, including the pressure-volume relationship of the injected fluid and the displacement field between adjacent lamellae. In this study, we develop a data-driven surrogate model of the delamination process for differential strut distributions using DeepONet, a new operator-regression neural network. This surrogate model is trained to predict the pressure-volume curve of the injected fluid and the damage progression within the wall given a spatial distribution of struts, with in silico data generated using a phase-field finite-element model. The results show that DeepONet can provide accurate predictions for diverse strut distributions, indicating that this composite branch-trunk neural network can effectively extract the underlying functional relationship between distinctive microstructures and their mechanical properties. More broadly, DeepONet can facilitate surrogate model-based analyses to quantify biological variability, improve inverse design and predict mechanical properties based on multi-modality experimental data.
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Affiliation(s)
- Minglang Yin
- Center for Biomedical Engineering, Brown University, Providence, RI 02912, USA
- School of Engineering, Brown University, Providence, RI 02912, USA
| | - Ehsan Ban
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Bruno V. Rego
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Enrui Zhang
- Division of Applied Mathematics, Brown University, Providence, RI 02912, USA
| | - Cristina Cavinato
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Jay D. Humphrey
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - George Em Karniadakis
- School of Engineering, Brown University, Providence, RI 02912, USA
- Division of Applied Mathematics, Brown University, Providence, RI 02912, USA
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Ferdian E, Dubowitz DJ, Mauger CA, Wang A, Young AA. WSSNet: Aortic Wall Shear Stress Estimation Using Deep Learning on 4D Flow MRI. Front Cardiovasc Med 2022; 8:769927. [PMID: 35141290 PMCID: PMC8818720 DOI: 10.3389/fcvm.2021.769927] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
Wall shear stress (WSS) is an important contributor to vessel wall remodeling and atherosclerosis. However, image-based WSS estimation from 4D Flow MRI underestimates true WSS values, and the accuracy is dependent on spatial resolution, which is limited in 4D Flow MRI. To address this, we present a deep learning algorithm (WSSNet) to estimate WSS trained on aortic computational fluid dynamics (CFD) simulations. The 3D CFD velocity and coordinate point clouds were resampled into a 2D template of 48 × 93 points at two inward distances (randomly varied from 0.3 to 2.0 mm) from the vessel surface (“velocity sheets”). The algorithm was trained on 37 patient-specific geometries and velocity sheets. Results from 6 validation and test cases showed high accuracy against CFD WSS (mean absolute error 0.55 ± 0.60 Pa, relative error 4.34 ± 4.14%, 0.92 ± 0.05 Pearson correlation) and noisy synthetic 4D Flow MRI at 2.4 mm resolution (mean absolute error 0.99 ± 0.91 Pa, relative error 7.13 ± 6.27%, and 0.79 ± 0.10 Pearson correlation). Furthermore, the method was applied on in vivo 4D Flow MRI cases, effectively estimating WSS from standard clinical images. Compared with the existing parabolic fitting method, WSSNet estimates showed 2–3 × higher values, closer to CFD, and a Pearson correlation of 0.68 ± 0.12. This approach, considering both geometric and velocity information from the image, is capable of estimating spatiotemporal WSS with varying image resolution, and is more accurate than existing methods while still preserving the correct WSS pattern distribution.
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Affiliation(s)
- Edward Ferdian
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- *Correspondence: Edward Ferdian
| | - David J. Dubowitz
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Charlene A. Mauger
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Alan Wang
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Alistair A. Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Department of Biomedical Engineering, King's College London, London, United Kingdom
- Alistair A. Young
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50
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Review on Y6-Based Semiconductor Materials and Their Future Development via Machine Learning. CRYSTALS 2022. [DOI: 10.3390/cryst12020168] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Non-fullerene acceptors are promising to achieve high efficiency in organic solar cells (OSCs). Y6-based acceptors, one group of new n-type semiconductors, have triggered tremendous attention when they reported a power-conversion efficiency (PCE) of 15.7% in 2019. After that, scientists are trying to improve the efficiency in different aspects including choosing new donors, tuning Y6 structures, and device engineering. In this review, we first summarize the properties of Y6 materials and the seven critical methods modifying the Y6 structure to improve the PCEs developed in the latest three years as well as the basic principles and parameters of OSCs. Finally, the authors would share perspectives on possibilities, necessities, challenges, and potential applications for designing multifunctional organic device with desired performances via machine learning.
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