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Kong F, Stocker S, Choi PS, Ma M, Ennis DB, Marsden AL. SDF4CHD: Generative modeling of cardiac anatomies with congenital heart defects. Med Image Anal 2024; 97:103293. [PMID: 39146700 PMCID: PMC11372630 DOI: 10.1016/j.media.2024.103293] [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: 11/08/2023] [Revised: 05/07/2024] [Accepted: 07/30/2024] [Indexed: 08/17/2024]
Abstract
Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural abnormalities, often requiring customized treatment plans for individual patients. Computational modeling and analysis of these unique cardiac anatomies can improve diagnosis and treatment planning and may ultimately lead to improved outcomes. Deep learning (DL) methods have demonstrated the potential to enable efficient treatment planning by automating cardiac segmentation and mesh construction for patients with normal cardiac anatomies. However, CHDs are often rare, making it challenging to acquire sufficiently large patient cohorts for training such DL models. Generative modeling of cardiac anatomies has the potential to fill this gap via the generation of virtual cohorts; however, prior approaches were largely designed for normal anatomies and cannot readily capture the significant topological variations seen in CHD patients. Therefore, we propose a type- and shape-disentangled generative approach suitable to capture the wide spectrum of cardiac anatomies observed in different CHD types and synthesize differently shaped cardiac anatomies that preserve the unique topology for specific CHD types. Our DL approach represents generic whole heart anatomies with CHD type-specific abnormalities implicitly using signed distance fields (SDF) based on CHD type diagnosis. To capture the shape-specific variations, we then learn invertible deformations to morph the learned CHD type-specific anatomies and reconstruct patient-specific shapes. After training with a dataset containing the cardiac anatomies of 67 patients spanning 6 CHD types and 14 combinations of CHD types, our method successfully captures divergent anatomical variations across different types and the meaningful intermediate CHD states across the spectrum of related CHD diagnoses. Additionally, our method demonstrates superior performance in CHD anatomy generation in terms of CHD-type correctness and shape plausibility. It also exhibits comparable generalization performance when reconstructing unseen cardiac anatomies. Moreover, our approach shows potential in augmenting image-segmentation pairs for rarer CHD types to significantly enhance cardiac segmentation accuracy for CHDs. Furthermore, it enables the generation of CHD cardiac meshes for computational simulation, facilitating a systematic examination of the impact of CHDs on cardiac functions.
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Affiliation(s)
- Fanwei Kong
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University, Stanford, CA, USA; Cardiovascular Institute, Stanford University, Stanford, CA, USA.
| | - Sascha Stocker
- Department of Radiology, Stanford University, Stanford, CA, USA; Institute for Biomedical Engineering, ETH Zurich and University Zurich, Zurich, Switzerland
| | - Perry S Choi
- Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, USA
| | - Michael Ma
- Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, USA
| | - Daniel B Ennis
- Cardiovascular Institute, Stanford University, Stanford, CA, USA; Department of Radiology, Stanford University, Stanford, CA, USA
| | - Alison L Marsden
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University, Stanford, CA, USA; Cardiovascular Institute, Stanford University, Stanford, CA, USA; Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, USA.
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2
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Salih AM, Galazzo IB, Gkontra P, Rauseo E, Lee AM, Lekadir K, Radeva P, Petersen SE, Menegaz G. A review of evaluation approaches for explainable AI with applications in cardiology. Artif Intell Rev 2024; 57:240. [PMID: 39132011 PMCID: PMC11315784 DOI: 10.1007/s10462-024-10852-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/03/2024] [Indexed: 08/13/2024]
Abstract
Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-024-10852-w.
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Affiliation(s)
- Ahmed M. Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
- Department of Population Health Sciences, University of Leicester, University Rd, Leicester, LE1 7RH UK
- Department of Computer Science, University of Zakho, Duhok road, Zakho, Kurdistan Iraq
| | - Ilaria Boscolo Galazzo
- Department of Engineering for Innovative Medicine, University of Verona, S. Francesco, 22, 37129 Verona, Italy
| | - Polyxeni Gkontra
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
| | - Elisa Rauseo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Aaron Mark Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, Spain
| | - Petia Radeva
- Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Gran Via de les Corts Catalanes, 585, 08007 Barcelona, Spain
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, UK
- Health Data Research, London, UK
- Alan Turing Institute, London, UK
| | - Gloria Menegaz
- Department of Engineering for Innovative Medicine, University of Verona, S. Francesco, 22, 37129 Verona, Italy
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3
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Beetz M, Banerjee A, Grau V. Modeling 3D Cardiac Contraction and Relaxation With Point Cloud Deformation Networks. IEEE J Biomed Health Inform 2024; 28:4810-4819. [PMID: 38648144 DOI: 10.1109/jbhi.2024.3389871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Global single-valued biomarkers, such as ejection fraction, are widely used in clinical practice to assess cardiac function. However, they only approximate the heart's true 3D deformation process, thus limiting diagnostic accuracy and the understanding of cardiac mechanics. Metrics based on 3D shape have been proposed to alleviate these shortcomings. In this work, we present the Point Cloud Deformation Network (PCD-Net) as a novel geometric deep learning approach for direct modeling of 3D cardiac mechanics of the biventricular anatomy between the extreme ends of the cardiac cycle. Its encoder-decoder architecture combines a low-dimensional latent space with recent advances in point cloud deep learning for effective multi-scale feature learning directly on flexible and memory-efficient point cloud representations of the cardiac anatomy. We first evaluate the PCD-Net's predictive capability for both cardiac contraction and relaxation on a large UK Biobank dataset of over 10,000 subjects and find average Chamfer distances between the predicted and ground truth anatomies below the pixel resolution of the underlying image acquisition. We then show the PCD-Net's ability to capture subpopulation-specific differences in 3D cardiac mechanics between normal and myocardial infarction (MI) subjects and visualize abnormal phenotypes between predicted normal 3D shapes and corresponding observed ones. Finally, we demonstrate that the PCD-Net's learned 3D deformation encodings outperform multiple clinical and machine learning benchmarks by 11% in terms of area under the receiver operating characteristic curve for the tasks of prevalent MI detection and incident MI prediction and by 7% in terms of Harrell's concordance index for MI survival analysis.
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Sharp AJ, Betts TR, Banerjee A. Leveraging 3D Atrial Geometry for the Evaluation of Atrial Fibrillation: A Comprehensive Review. J Clin Med 2024; 13:4442. [PMID: 39124709 PMCID: PMC11313299 DOI: 10.3390/jcm13154442] [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: 06/28/2024] [Revised: 07/19/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia associated with significant morbidity and mortality. Managing risk of stroke and AF burden are pillars of AF management. Atrial geometry has long been recognized as a useful measure in achieving these goals. However, traditional diagnostic approaches often overlook the complex spatial dynamics of the atria. This review explores the emerging role of three-dimensional (3D) atrial geometry in the evaluation and management of AF. Advancements in imaging technologies and computational modeling have enabled detailed reconstructions of atrial anatomy, providing insights into the pathophysiology of AF that were previously unattainable. We examine current methodologies for interpreting 3D atrial data, including qualitative, basic quantitative, global quantitative, and statistical shape modeling approaches. We discuss their integration into clinical practice, highlighting potential benefits such as personalized treatment strategies, improved outcome prediction, and informed treatment approaches. Additionally, we discuss the challenges and limitations associated with current approaches, including technical constraints and variable interpretations, and propose future directions for research and clinical applications. This comprehensive review underscores the transformative potential of leveraging 3D atrial geometry in the evaluation and management of AF, advocating for its broader adoption in clinical practice.
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Affiliation(s)
- Alexander J. Sharp
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
| | - Timothy R. Betts
- Cardiology Department, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Abhirup Banerjee
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK
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Temsah MH, Alhuzaimi AN, Almansour M, Aljamaan F, Alhasan K, Batarfi MA, Altamimi I, Alharbi A, Alsuhaibani AA, Alwakeel L, Alzahrani AA, Alsulaim KB, Jamal A, Khayat A, Alghamdi MH, Halwani R, Khan MK, Al-Eyadhy A, Nazer R. Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases. J Med Syst 2024; 48:54. [PMID: 38780839 DOI: 10.1007/s10916-024-02072-0] [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: 01/24/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024]
Abstract
Artificial Intelligence (AI), particularly AI-Generated Imagery, has the potential to impact medical and patient education. This research explores the use of AI-generated imagery, from text-to-images, in medical education, focusing on congenital heart diseases (CHD). Utilizing ChatGPT's DALL·E 3, the research aims to assess the accuracy and educational value of AI-created images for 20 common CHDs. In this study, we utilized DALL·E 3 to generate a comprehensive set of 110 images, comprising ten images depicting the normal human heart and five images for each of the 20 common CHDs. The generated images were evaluated by a diverse group of 33 healthcare professionals. This cohort included cardiology experts, pediatricians, non-pediatric faculty members, trainees (medical students, interns, pediatric residents), and pediatric nurses. Utilizing a structured framework, these professionals assessed each image for anatomical accuracy, the usefulness of in-picture text, its appeal to medical professionals, and the image's potential applicability in medical presentations. Each item was assessed on a Likert scale of three. The assessments produced a total of 3630 images' assessments. Most AI-generated cardiac images were rated poorly as follows: 80.8% of images were rated as anatomically incorrect or fabricated, 85.2% rated to have incorrect text labels, 78.1% rated as not usable for medical education. The nurses and medical interns were found to have a more positive perception about the AI-generated cardiac images compared to the faculty members, pediatricians, and cardiology experts. Complex congenital anomalies were found to be significantly more predicted to anatomical fabrication compared to simple cardiac anomalies. There were significant challenges identified in image generation. Based on our findings, we recommend a vigilant approach towards the use of AI-generated imagery in medical education at present, underscoring the imperative for thorough validation and the importance of collaboration across disciplines. While we advise against its immediate integration until further validations are conducted, the study advocates for future AI-models to be fine-tuned with accurate medical data, enhancing their reliability and educational utility.
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Affiliation(s)
- Mohamad-Hani Temsah
- College of Medicine, King Saud University, Riyadh, Saudi Arabia.
- Pediatric Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia.
- Evidence-Based Health Care & Knowledge Translation Research Chair, Family & Community Medicine Department, College of Medicine, King Saud University, 11362, Riyadh, Saudi Arabia.
| | - Abdullah N Alhuzaimi
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Division of Pediatric Cardiology, Cardiac Science Department, College of Medicine, King Saud University Medical City, 11362, Riyadh, Saudi Arabia
| | - Mohammed Almansour
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Department of Medical Education, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Fadi Aljamaan
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Critical Care Department, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Khalid Alhasan
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Pediatric Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
- Kidney & Pancreas Health Center, Organ Transplant Center of Excellence, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Munirah A Batarfi
- Basic Medical Sciences, College of Medicine King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | | | - Amani Alharbi
- Pediatric Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
| | | | - Leena Alwakeel
- Pediatric Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
| | | | | | - Amr Jamal
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Evidence-Based Health Care & Knowledge Translation Research Chair, Family & Community Medicine Department, College of Medicine, King Saud University, 11362, Riyadh, Saudi Arabia
- Department of Family and Community Medicine, King Saud University Medical City, 11362, Riyadh, Saudi Arabia
| | - Afnan Khayat
- Health Information Management Department, Prince Sultan Military College of Health Sciences, Al Dhahran, Saudi Arabia
| | - Mohammed Hussien Alghamdi
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Division of Pediatric Cardiology, Cardiac Science Department, College of Medicine, King Saud University Medical City, 11362, Riyadh, Saudi Arabia
- Department of Medical Education, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Rabih Halwani
- Department of Clinical Sciences, College of Medicine, University of Sharjah, 27272, Sharjah, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, 27272, Sharjah, United Arab Emirates
| | - Muhammad Khurram Khan
- Center of Excellence in Information Assurance, King Saud University, 11653, Riyadh, Saudi Arabia
| | - Ayman Al-Eyadhy
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Pediatric Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
| | - Rakan Nazer
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Department of Cardiac Science, King Fahad Cardiac Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia
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Kolk MZH, Ruipérez-Campillo S, Alvarez-Florez L, Deb B, Bekkers EJ, Allaart CP, Van Der Lingen ALCJ, Clopton P, Išgum I, Wilde AAM, Knops RE, Narayan SM, Tjong FVY. Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator. EBioMedicine 2024; 99:104937. [PMID: 38118401 PMCID: PMC10772563 DOI: 10.1016/j.ebiom.2023.104937] [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: 06/30/2023] [Revised: 10/20/2023] [Accepted: 12/12/2023] [Indexed: 12/22/2023] Open
Abstract
BACKGROUND Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. METHODS A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. FINDINGS 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. INTERPRETATION Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. FUNDING This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).
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Affiliation(s)
- Maarten Z H Kolk
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Samuel Ruipérez-Campillo
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA; Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology Zurich (ETHz), Gloriastrasse 35, Zurich, Switzerland; ITACA Institute, Universtitat Politècnica de València, Camino de Vera S/n, Valencia, Spain
| | - Laura Alvarez-Florez
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Brototo Deb
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Erik J Bekkers
- Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, the Netherlands
| | - Cornelis P Allaart
- Department of Cardiology, Amsterdam UMC, Location VU Medical Center, De Boelelaan 1118, Amsterdam, the Netherlands
| | | | - Paul Clopton
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Arthur A M Wilde
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Reinoud E Knops
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands
| | - Sanjiv M Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Fleur V Y Tjong
- Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands; Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA; Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam, the Netherlands.
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7
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Kong F, Stocker S, Choi PS, Ma M, Ennis DB, Marsden A. SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defects. ARXIV 2023:arXiv:2311.00332v2. [PMID: 37961745 PMCID: PMC10635288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural abnormalities, often requiring customized treatment plans for individual patients. Computational modeling and analysis of these unique cardiac anatomies can improve diagnosis and treatment planning and may ultimately lead to improved outcomes. Deep learning (DL) methods have demonstrated the potential to enable efficient treatment planning by automating cardiac segmentation and mesh construction for patients with normal cardiac anatomies. However, CHDs are often rare, making it challenging to acquire sufficiently large patient cohorts for training such DL models. Generative modeling of cardiac anatomies has the potential to fill this gap via the generation of virtual cohorts; however, prior approaches were largely designed for normal anatomies and cannot readily capture the significant topological variations seen in CHD patients. Therefore, we propose a type- and shape-disentangled generative approach suitable to capture the wide spectrum of cardiac anatomies observed in different CHD types and synthesize differently shaped cardiac anatomies that preserve the unique topology for specific CHD types. Our DL approach represents generic whole heart anatomies with CHD type-specific abnormalities implicitly using signed distance fields (SDF) based on CHD type diagnosis, which conveniently captures divergent anatomical variations across different types and represents meaningful intermediate CHD states. To capture the shape-specific variations, we then learn invertible deformations to morph the learned CHD type-specific anatomies and reconstruct patient-specific shapes. Our approach has the potential to augment the image-segmentation pairs for rarer CHD types for cardiac segmentation and generate cohorts of CHD cardiac meshes for computational simulation.
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Affiliation(s)
- Fanwei Kong
- Department of Pediatrics, Cardiovascular Institute, Stanford University, Stanford
| | - Sascha Stocker
- Department of Radiology, Stanford University, Stanford
- Institute for Biomedical Engineering, ETH Zurich and University Zurich, Zurich
| | - Perry S Choi
- Department of Cardiothoracic Surgery, Stanford University, Stanford
| | - Michael Ma
- Department of Cardiothoracic Surgery, Stanford University, Stanford
| | - Daniel B Ennis
- Department of Radiology, Cardiovascular Institute, Stanford University, Stanford
| | - Alison Marsden
- Department of Bioengineering, Department of Mechanical Engineering, Department of Pediatrics, Stanford University, Stanford
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Beetz M, Yang Y, Banerjee A, Li L, Grau V. 3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082756 DOI: 10.1109/embc40787.2023.10340878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases with associated clinical decision-making typically based on single-valued imaging biomarkers. However, such metrics only approximate the complex 3D structure and physiology of the heart and hence hinder a better understanding and prediction of MI outcomes. In this work, we investigate the utility of complete 3D cardiac shapes in the form of point clouds for an improved detection of MI events. To this end, we propose a fully automatic multi-step pipeline consisting of a 3D cardiac surface reconstruction step followed by a point cloud classification network. Our method utilizes recent advances in geometric deep learning on point clouds to enable direct and efficient multi-scale learning on high-resolution surface models of the cardiac anatomy. We evaluate our approach on 1068 UK Biobank subjects for the tasks of prevalent MI detection and incident MI prediction and find improvements of ∼13% and ∼5% respectively over clinical benchmarks. Furthermore, we analyze the role of each ventricle and cardiac phase for 3D shape-based MI detection and conduct a visual analysis of the morphological and physiological patterns typically associated with MI outcomes.Clinical relevance- The presented approach enables the fast and fully automatic pathology-specific analysis of full 3D cardiac shapes. It can thus be employed as a real-time diagnostic tool in clinical practice to discover and visualize more intricate biomarkers than currently used single-valued metrics and improve predictive accuracy of myocardial infarction.
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Krittanawong C, Omar AMS, Narula S, Sengupta PP, Glicksberg BS, Narula J, Argulian E. Deep Learning for Echocardiography: Introduction for Clinicians and Future Vision: State-of-the-Art Review. Life (Basel) 2023; 13:1029. [PMID: 37109558 PMCID: PMC10145844 DOI: 10.3390/life13041029] [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: 02/17/2023] [Revised: 03/30/2023] [Accepted: 04/03/2023] [Indexed: 04/29/2023] Open
Abstract
Exponential growth in data storage and computational power is rapidly narrowing the gap between translating findings from advanced clinical informatics into cardiovascular clinical practice. Specifically, cardiovascular imaging has the distinct advantage in providing a great quantity of data for potentially rich insights, but nuanced interpretation requires a high-level skillset that few individuals possess. A subset of machine learning, deep learning (DL), is a modality that has shown promise, particularly in the areas of image recognition, computer vision, and video classification. Due to a low signal-to-noise ratio, echocardiographic data tend to be challenging to classify; however, utilization of robust DL architectures may help clinicians and researchers automate conventional human tasks and catalyze the extraction of clinically useful data from the petabytes of collected imaging data. The promise is extending far and beyond towards a contactless echocardiographic exam-a dream that is much needed in this time of uncertainty and social distancing brought on by a stunning pandemic culture. In the current review, we discuss state-of-the-art DL techniques and architectures that can be used for image and video classification, and future directions in echocardiographic research in the current era.
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Affiliation(s)
- Chayakrit Krittanawong
- Cardiology Division, NYU Langone Health, NYU School of Medicine, New York, NY 10016, USA
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
| | - Alaa Mabrouk Salem Omar
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
| | - Sukrit Narula
- Department of Medicine, Yale School of Medicine, New Haven, CT 06512, USA
| | - Partho P. Sengupta
- Robert Wood Johnson University Hospital, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
| | - Benjamin S. Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jagat Narula
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
| | - Edgar Argulian
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
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