1
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Rajagopal S, Bogaard HJ, Elbaz MSM, Freed BH, Remy-Jardin M, van Beek EJR, Gopalan D, Kiely DG. Emerging multimodality imaging techniques for the pulmonary circulation. Eur Respir J 2024:2401128. [PMID: 39209480 DOI: 10.1183/13993003.01128-2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Accepted: 06/11/2024] [Indexed: 09/04/2024]
Abstract
Pulmonary hypertension (PH) remains a challenging condition to diagnose, classify and treat. Current approaches to the assessment of PH include echocardiography, ventilation/perfusion scintigraphy, cross-sectional imaging using computed tomography and magnetic resonance imaging, and right heart catheterisation. However, these approaches only provide an indirect readout of the primary pathology of the disease: abnormal vascular remodelling in the pulmonary circulation. With the advent of newer imaging techniques, there is a shift toward increased utilisation of noninvasive high-resolution modalities that offer a more comprehensive cardiopulmonary assessment and improved visualisation of the different components of the pulmonary circulation. In this review, we explore advances in imaging of the pulmonary vasculature and their potential clinical translation. These include advances in diagnosis and assessing treatment response, as well as strategies that allow reduced radiation exposure and implementation of artificial intelligence technology. These emerging modalities hold the promise of developing a deeper understanding of pulmonary vascular disease and the impact of comorbidities. They also have the potential to improve patient outcomes by reducing time to diagnosis, refining classification, monitoring treatment response and improving our understanding of disease mechanisms.
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Affiliation(s)
| | - Harm J Bogaard
- Department of Pulmonology, Amsterdam University Medical Center, Location VU Medical Center, Amsterdam, The Netherlands
| | - Mohammed S M Elbaz
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Benjamin H Freed
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Edwin J R van Beek
- Edinburgh Imaging, Queens Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Deepa Gopalan
- Department of Radiology, Imperial College Healthcare NHS Trust, London, UK
| | - David G Kiely
- Sheffield Pulmonary Vascular Disease Unit and NIHR Biomedical Research Centre Sheffield, Royal Hallamshire Hospital, Sheffield, UK
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2
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Liu J, Du H, Huang L, Xie W, Liu K, Zhang X, Chen S, Zhang Y, Li D, Pan H. AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery. ACS APPLIED MATERIALS & INTERFACES 2024; 16:38832-38851. [PMID: 39016521 DOI: 10.1021/acsami.4c07665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2024]
Abstract
Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.
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Affiliation(s)
- Junchi Liu
- Department of Anesthesiology, The First Hospital of Jilin University, 71 Xinmin Street, Changchun 130012, China
| | - Hanze Du
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Lei Huang
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Wangni Xie
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Kexuan Liu
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Xue Zhang
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Shi Chen
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Yuan Zhang
- Department of Anesthesiology, The First Hospital of Jilin University, 71 Xinmin Street, Changchun 130012, China
| | - Daowei Li
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Hui Pan
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
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3
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Rivera Boadla ME, Sharma NR, Varghese J, Lamichhane S, Khan MH, Gulati A, Khurana S, Tan S, Sharma A. Multimodal Cardiac Imaging Revisited by Artificial Intelligence: An Innovative Way of Assessment or Just an Aid? Cureus 2024; 16:e64272. [PMID: 39130913 PMCID: PMC11315592 DOI: 10.7759/cureus.64272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/10/2024] [Indexed: 08/13/2024] Open
Abstract
Cardiovascular disease remains a leading global health challenge, necessitating advanced diagnostic approaches. This review explores the integration of artificial intelligence (AI) in multimodal cardiac imaging, tracing its evolution from early X-rays to contemporary techniques such as CT, MRI, and nuclear imaging. AI, particularly machine learning and deep learning, significantly enhances cardiac diagnostics by estimating biological heart age, predicting disease risk, and optimizing heart failure management through adaptive algorithms without explicit programming or feature engineering. Key contributions include AI's transformative role in non-invasive coronary artery disease diagnosis, arrhythmia detection via wearable devices, and personalized treatment strategies. Despite substantial progress, challenges including data standardization, algorithm validation, regulatory approval, and ethical considerations must be addressed to fully harness AI's potential. Collaborative efforts among clinicians, scientists, industry stakeholders, and regulatory bodies are essential for the safe and effective deployment of AI in cardiac imaging, promising enhanced diagnostics and personalized patient care.
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Affiliation(s)
| | - Nava R Sharma
- Internal Medicine, Maimonides Medical Center, Brooklyn, USA
- Medicine, Manipal College of Medical Sciences, Pokhara, NPL
| | - Jeffy Varghese
- Internal Medicine, Maimonides Medical Center, Brooklyn, USA
| | - Saral Lamichhane
- Internal Medicine, NYC Health + Hospitals/Woodhull, Brooklyn, USA
- Internal Medicine, Gandaki Medical College, Pokhara, NPL
| | | | - Amit Gulati
- Cardiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | | | - Samuel Tan
- Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Anupam Sharma
- Hematology and Oncology, Fortis Hospital, Noida, IND
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4
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Aromiwura AA, Cavalcante JL, Kwong RY, Ghazipour A, Amini A, Bax J, Raman S, Pontone G, Kalra DK. The role of artificial intelligence in cardiovascular magnetic resonance imaging. Prog Cardiovasc Dis 2024:S0033-0620(24)00092-6. [PMID: 38925255 DOI: 10.1016/j.pcad.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Accepted: 06/23/2024] [Indexed: 06/28/2024]
Abstract
Cardiovascular magnetic resonance (CMR) imaging is the gold standard test for myocardial tissue characterization and chamber volumetric and functional evaluation. However, manual CMR analysis can be time-consuming and is subject to intra- and inter-observer variability. Artificial intelligence (AI) is a field that permits automated task performance through the identification of high-level and complex data relationships. In this review, we review the rapidly growing role of AI in CMR, including image acquisition, sequence prescription, artifact detection, reconstruction, segmentation, and data reporting and analysis including quantification of volumes, function, myocardial infarction (MI) and scar detection, and prediction of outcomes. We conclude with a discussion of the emerging challenges to widespread adoption and solutions that will allow for successful, broader uptake of this powerful technology.
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Affiliation(s)
| | | | - Raymond Y Kwong
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Aryan Ghazipour
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - Amir Amini
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - Jeroen Bax
- Department of Cardiology, Leiden University, Leiden, the Netherlands
| | - Subha Raman
- Division of Cardiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Gianluca Pontone
- Department of Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, University of Milan, Milan, Italy
| | - Dinesh K Kalra
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA.
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5
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Suresh V, Singh KK, Vaish E, Gurjar M, Ambuli Nambi A, Khulbe Y, Muzaffar S. Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool. Cureus 2024; 16:e59797. [PMID: 38846182 PMCID: PMC11154024 DOI: 10.7759/cureus.59797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/07/2024] [Indexed: 06/09/2024] Open
Abstract
Artificial intelligence (AI) is a technique that attempts to replicate human intelligence, analytical behavior, and decision-making ability. This includes machine learning, which involves the use of algorithms and statistical techniques to enhance the computer's ability to make decisions more accurately. Due to AI's ability to analyze, comprehend, and interpret considerable volumes of data, it has been increasingly used in the field of healthcare. In critical care medicine, where most of the patient load requires timely interventions due to the perilous nature of the condition, AI's ability to monitor, analyze, and predict unfavorable outcomes is an invaluable asset. It can significantly improve timely interventions and prevent unfavorable outcomes, which, otherwise, is not always achievable owing to the constrained human ability to multitask with optimum efficiency. AI has been implicated in intensive care units over the past many years. In addition to its advantageous applications, this article discusses its disadvantages, prospects, and the changes needed to train future critical care professionals. A comprehensive search of electronic databases was performed using relevant keywords. Data from articles pertinent to the topic was assimilated into this review article.
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Affiliation(s)
- Vinay Suresh
- General Medicine and Surgery, King George's Medical University, Lucknow, IND
| | - Kaushal K Singh
- General Medicine, King George's Medical University, Lucknow, IND
| | - Esha Vaish
- Internal Medicine, Mount Sinai Morningside West, New York, USA
| | - Mohan Gurjar
- Critical Care Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, IND
| | | | - Yashita Khulbe
- General Medicine and Surgery, King George's Medical University, Lucknow, IND
| | - Syed Muzaffar
- Critical Care Medicine, King George's Medical University, Lucknow, IND
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6
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Dou J, Jiang N, Zeng J, Wang S, Tian S, Shan S, Li Y, Xu Z, Lin X, Jin S, Dong J, Chen H. Novel 3D morphological characteristics for congenital biliary dilatation diagnosis: a case-control study. Int J Surg 2024; 110:2614-2624. [PMID: 38376858 DOI: 10.1097/js9.0000000000001204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 02/04/2024] [Indexed: 02/21/2024]
Abstract
BACKGROUND Congenital biliary dilatation (CBD) necessitates the timely removal of dilated bile ducts. Accurate differentiation between CBD and secondary biliary dilatation (SBD) is crucial for treatment decisions, and identification of CBD with intrahepatic involvement is vital for surgical planning and supportive care. This study aimed to develop quantitative models based on bile duct morphology to distinguish CBD from SBD and further identify CBD with intrahepatic involvement. MATERIALS AND METHODS The retrospective study included 131 CBD and 209 SBD patients between December 2014 and December 2021 for model development, internal validation, and testing. A separate cohort of 15 CBD and 34 SBD patients between January 2022 and December 2022 was recruited for temporally-independent validation. Quantitative shape-based (Shape) and diameter-based (Diam) morphological characteristics of bile ducts were extracted to build a CBD diagnosis model to distinguish CBD from SBD and an intrahepatic involvement identification model to classify CBD with/without intrahepatic involvement. The diagnostic performance of the models was compared with that of experienced hepatobiliary surgeons. RESULTS The CBD diagnosis model using clinical, Shape, and Diam characteristics showed good performance with an AUROC of 0.942 (95% CI: 0.890-0.994), AUPRC of 0.917 (0.855-0.979), accuracy of 0.891, sensitivity of 0.950, and F1-score of 0.864. The model outperformed two experienced surgeons in accuracy, sensitivity, and F1-score. The intrahepatic involvement identification model using clinical, Shape, and Diam characteristics yielded outstanding performance with an AUROC of 0.944 (0.879-1.000), AUPRC of 0.982 (0.947-1.000), accuracy of 0.932, sensitivity of 0.971, and F1-score of 0.957. The models demonstrated generalizable performance on the temporally-independent validation cohort. CONCLUSIONS This study developed two robust quantitative models for distinguishing CBD from SBD and identifying CBD with intrahepatic involvement, respectively, based on morphological characteristics of the bile ducts, showing great potential in risk stratification and surgical planning of CBD.
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Affiliation(s)
- Jiaqi Dou
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Nan Jiang
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Jianping Zeng
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Siyuan Wang
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Song Tian
- Philips Healthcare, Beijing, People's Republic of China
| | - Siqiao Shan
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Yuze Li
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Ziming Xu
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Xiaoqi Lin
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Shuo Jin
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Jiahong Dong
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Huijun Chen
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
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7
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Ragnarsdottir H, Ozkan E, Michel H, Chin-Cheong K, Manduchi L, Wellmann S, Vogt JE. Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms. Int J Comput Vis 2024; 132:2567-2584. [PMID: 38911323 PMCID: PMC11186939 DOI: 10.1007/s11263-024-01996-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 01/04/2024] [Indexed: 06/25/2024]
Abstract
Pulmonary hypertension (PH) in newborns and infants is a complex condition associated with several pulmonary, cardiac, and systemic diseases contributing to morbidity and mortality. Thus, accurate and early detection of PH and the classification of its severity is crucial for appropriate and successful management. Using echocardiography, the primary diagnostic tool in pediatrics, human assessment is both time-consuming and expertise-demanding, raising the need for an automated approach. Little effort has been directed towards automatic assessment of PH using echocardiography, and the few proposed methods only focus on binary PH classification on the adult population. In this work, we present an explainable multi-view video-based deep learning approach to predict and classify the severity of PH for a cohort of 270 newborns using echocardiograms. We use spatio-temporal convolutional architectures for the prediction of PH from each view, and aggregate the predictions of the different views using majority voting. Our results show a mean F1-score of 0.84 for severity prediction and 0.92 for binary detection using 10-fold cross-validation and 0.63 for severity prediction and 0.78 for binary detection on the held-out test set. We complement our predictions with saliency maps and show that the learned model focuses on clinically relevant cardiac structures, motivating its usage in clinical practice. To the best of our knowledge, this is the first work for an automated assessment of PH in newborns using echocardiograms.
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Affiliation(s)
- Hanna Ragnarsdottir
- Department of Computer Science, ETH Zurich, Universitätstrasse 6, 8092 Zürich, Switzerland
| | - Ece Ozkan
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 43 Vassar Street, Cambridge, MA 02139 USA
| | - Holger Michel
- Department of Neonatology, University Children’s Hospital Regensburg (KUNO), Hospital St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, Germany
| | - Kieran Chin-Cheong
- Department of Computer Science, ETH Zurich, Universitätstrasse 6, 8092 Zürich, Switzerland
| | - Laura Manduchi
- Department of Computer Science, ETH Zurich, Universitätstrasse 6, 8092 Zürich, Switzerland
| | - Sven Wellmann
- Department of Neonatology, University Children’s Hospital Regensburg (KUNO), Hospital St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, Germany
| | - Julia E. Vogt
- Department of Computer Science, ETH Zurich, Universitätstrasse 6, 8092 Zürich, Switzerland
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8
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Bourazana A, Xanthopoulos A, Briasoulis A, Magouliotis D, Spiliopoulos K, Athanasiou T, Vassilopoulos G, Skoularigis J, Triposkiadis F. Artificial Intelligence in Heart Failure: Friend or Foe? Life (Basel) 2024; 14:145. [PMID: 38276274 PMCID: PMC10817517 DOI: 10.3390/life14010145] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians' hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.
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Affiliation(s)
- Angeliki Bourazana
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | - Andrew Xanthopoulos
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | - Alexandros Briasoulis
- Division of Cardiovascular Medicine, Section of Heart Failure and Transplantation, University of Iowa, Iowa City, IA 52242, USA
| | - Dimitrios Magouliotis
- Department of Cardiothoracic Surgery, University of Thessaly, 41110 Larissa, Greece; (D.M.); (K.S.)
| | - Kyriakos Spiliopoulos
- Department of Cardiothoracic Surgery, University of Thessaly, 41110 Larissa, Greece; (D.M.); (K.S.)
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, St Mary’s Hospital, London W2 1NY, UK
| | - George Vassilopoulos
- Department of Hematology, University Hospital of Larissa, University of Thessaly Medical School, 41110 Larissa, Greece
| | - John Skoularigis
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
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9
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Zargarzadeh A, Javanshir E, Ghaffari A, Mosharkesh E, Anari B. Artificial intelligence in cardiovascular medicine: An updated review of the literature. J Cardiovasc Thorac Res 2023; 15:204-209. [PMID: 38357567 PMCID: PMC10862032 DOI: 10.34172/jcvtr.2023.33031] [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: 11/27/2023] [Accepted: 12/10/2023] [Indexed: 02/16/2024] Open
Abstract
Screening and early detection of cardiovascular disease (CVD) are crucial for managing progress and preventing related morbidity. In recent years, several studies have reported the important role of Artificial intelligence (AI) technology and its integration into various medical sectors. AI applications are able to deal with the massive amounts of data (medical records, ultrasounds, medications, and experimental results) generated in medicine and identify novel details that would otherwise be forgotten in the mass of healthcare data sets. Nowadays, AI algorithms are currently used to improve diagnosis of some CVDs including heart failure, atrial fibrillation, hypertrophic cardiomyopathy and pulmonary hypertension. This review summarized some AI concepts, critical execution requirements, obstacles, and new applications for CVDs.
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Affiliation(s)
| | - Elnaz Javanshir
- Cardiovascular Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Alireza Ghaffari
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Erfan Mosharkesh
- Faculty of Veterinary Medicine, University of Tabriz, Tabriz, Iran
| | - Babak Anari
- Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
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10
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Huang AA, Huang SY. Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension. J Clin Hypertens (Greenwich) 2023; 25:1135-1144. [PMID: 37971610 PMCID: PMC10710553 DOI: 10.1111/jch.14745] [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: 05/23/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/19/2023]
Abstract
Machine learning methods are widely used within the medical field to enhance prediction. However, little is known about the reliability and efficacy of these models to predict long-term medical outcomes such as blood pressure using lifestyle factors, such as diet. The authors assessed whether machine-learning techniques could accurately predict hypertension risk using nutritional information. A cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) between January 2017 and March 2020. XGBoost was used as the machine-learning model of choice in this study due to its increased performance relative to other common methods within medical studies. Model prediction metrics (e.g., AUROC, Balanced Accuracy) were used to measure overall model efficacy, covariate Gain statistics (percentage each covariate contributes to the overall prediction) and SHapely Additive exPlanations (SHAP, method to visualize each covariate) were used to provide explanations to machine-learning output and increase the transparency of this otherwise cryptic method. Of a total of 9650 eligible patients, the mean age was 41.02 (SD = 22.16), 4792 (50%) males, 4858 (50%) female, 3407 (35%) White patients, 2567 (27%) Black patients, 2108 (22%) Hispanic patients, and 981 (10%) Asian patients. From evaluation of model gain statistics, age was found to be the single strongest predictor of hypertension, with a gain of 53.1%. Additionally, demographic factors such as poverty and Black race were also strong predictors of hypertension, with gain of 4.33% and 4.18%, respectively. Nutritional Covariates contributed 37% to the overall prediction: Sodium, Caffeine, Potassium, and Alcohol intake being significantly represented within the model. Machine Learning can be used to predict hypertension.
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Affiliation(s)
- Alexander A. Huang
- Cornell UniversityNew YorkUSA
- Northwestern University Feinberg School of MedicineChicagoUSA
| | - Samuel Y. Huang
- Cornell UniversityNew YorkUSA
- Virginia Commonwealth University School of MedicineRichmondUSA
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11
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Gao Y, Zhou Z, Zhang B, Guo S, Bo K, Li S, Zhang N, Wang H, Yang G, Zhang H, Liu T, Xu L. Deep learning-based prognostic model using non-enhanced cardiac cine MRI for outcome prediction in patients with heart failure. Eur Radiol 2023; 33:8203-8213. [PMID: 37286789 DOI: 10.1007/s00330-023-09785-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 04/14/2023] [Accepted: 04/21/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVES To evaluate the performance of a deep learning-based multi-source model for survival prediction and risk stratification in patients with heart failure. METHODS Patients with heart failure with reduced ejection fraction (HFrEF) who underwent cardiac magnetic resonance between January 2015 and April 2020 were retrospectively included in this study. Baseline electronic health record data, including clinical demographic information, laboratory data, and electrocardiographic information, were collected. Short-axis non-contrast cine images of the whole heart were acquired to estimate the cardiac function parameters and the motion features of the left ventricle. Model accuracy was evaluated using the Harrell's concordance index. All patients were followed up for major adverse cardiac events (MACEs), and survival prediction was assessed using Kaplan-Meier curves. RESULTS A total of 329 patients were evaluated (age 54 ± 14 years; men, 254) in this study. During a median follow-up period of 1041 days, 62 patients experienced MACEs and their median survival time was 495 days. When compared with conventional Cox hazard prediction models, deep learning models showed better survival prediction performance. Multi-data denoising autoencoder (DAE) model reached the concordance index of 0.8546 (95% CI: 0.7902-0.8883). Furthermore, when divided into phenogroups, the multi-data DAE model could significantly discriminate between the survival outcomes of the high-risk and low-risk groups compared with other models (p < 0.001). CONCLUSIONS The proposed deep learning (DL) model based on non-contrast cardiac cine magnetic resonance imaging could independently predict the outcome of patients with HFrEF and showed better prediction efficiency than conventional methods. CLINICAL RELEVANCE STATEMENT The proposed multi-source deep learning model based on cardiac magnetic resonance enables survival prediction in patients with heart failure. KEY POINTS • A multi-source deep learning model based on non-contrast cardiovascular magnetic resonance (CMR) cine images was built to make robust survival prediction in patients with heart failure. • The ground truth definition contains electronic health record data as well as DL-based motion data, and cardiac motion information is extracted by optical flow method from non-contrast CMR cine images. • The DL-based model exhibits better prognostic value and stratification performance when compared with conventional prediction models and could aid in the risk stratification in patients with HF.
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Affiliation(s)
- Yifeng Gao
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No.2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Zhen Zhou
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No.2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Bing Zhang
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Saidi Guo
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Kairui Bo
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No.2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Shuang Li
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No.2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Nan Zhang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No.2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Hui Wang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No.2, Anzhen Road, Chaoyang District, Beijing, 100029, China
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Heye Zhang
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Tong Liu
- Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, No.2, Anzhen Road, Chaoyang District, Beijing, 100029, China.
| | - Lei Xu
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No.2, Anzhen Road, Chaoyang District, Beijing, 100029, China.
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12
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Bradley AJ, Ghawanmeh M, Govi AM, Covas P, Panjrath G, Choi AD. Emerging Roles for Artificial Intelligence in Heart Failure Imaging. Heart Fail Clin 2023; 19:531-543. [PMID: 37714592 DOI: 10.1016/j.hfc.2023.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/17/2023]
Abstract
Artificial intelligence (AI) applications are expanding in cardiac imaging. AI research has shown promise in workflow optimization, disease diagnosis, and integration of clinical and imaging data to predict patient outcomes. The diagnostic and prognostic paradigm of heart failure is heavily reliant on cardiac imaging. As AI becomes increasingly validated and integrated into clinical practice, AI influence on heart failure management will grow. This review discusses areas of current research and potential clinical applications in AI as applied to heart failure cardiac imaging.
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Affiliation(s)
- Andrew J Bradley
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
| | - Malik Ghawanmeh
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Ashley M Govi
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Pedro Covas
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Gurusher Panjrath
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA. https://twitter.com/PanjrathG
| | - Andrew D Choi
- Division of Cardiology, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA. https://twitter.com/AChoiHeart
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13
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Nashwan AJ. A New Era in Cardiometabolic Management: Unlocking the Potential of Artificial Intelligence for Improved Patient Outcomes. Endocr Pract 2023; 29:743-745. [PMID: 37328103 DOI: 10.1016/j.eprac.2023.06.003] [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: 05/04/2023] [Revised: 06/03/2023] [Accepted: 06/07/2023] [Indexed: 06/18/2023]
Affiliation(s)
- Abdulqadir J Nashwan
- Nursing Department, Hamad Medical Corporation, Doha, Qatar; Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar.
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14
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Mohsin SN, Gapizov A, Ekhator C, Ain NU, Ahmad S, Khan M, Barker C, Hussain M, Malineni J, Ramadhan A, Halappa Nagaraj R. The Role of Artificial Intelligence in Prediction, Risk Stratification, and Personalized Treatment Planning for Congenital Heart Diseases. Cureus 2023; 15:e44374. [PMID: 37664359 PMCID: PMC10469091 DOI: 10.7759/cureus.44374] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2023] [Indexed: 09/05/2023] Open
Abstract
This narrative review delves into the potential of artificial intelligence (AI) in predicting, stratifying risk, and personalizing treatment planning for congenital heart disease (CHD). CHD is a complex condition that affects individuals across various age groups. The review highlights the challenges in predicting risks, planning treatments, and prognosticating long-term outcomes due to CHD's multifaceted nature, limited data, ethical concerns, and individual variabilities. AI, with its ability to analyze extensive data sets, presents a promising solution. The review emphasizes the need for larger, diverse datasets, the integration of various data sources, and the analysis of longitudinal data. Prospective validation in real-world clinical settings, interpretability, and the importance of human clinical expertise are also underscored. The ethical considerations surrounding privacy, consent, bias, monitoring, and human oversight are examined. AI's implications include improved patient outcomes, cost-effectiveness, and real-time decision support. The review aims to provide a comprehensive understanding of AI's potential for revolutionizing CHD management and highlights the significance of collaboration and transparency to address challenges and limitations.
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Affiliation(s)
| | | | - Chukwuyem Ekhator
- Neuro-Oncology, New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, USA
| | - Noor U Ain
- Medicine, Mayo Hospital, Lahore, PAK
- Medicine, King Edward Medical University, Lahore, PAK
| | | | - Mavra Khan
- Medicine and Surgery, Mayo Hospital, Lahore , PAK
| | - Chad Barker
- Public Health, University of South Florida, Tampa, USA
| | | | - Jahnavi Malineni
- Medicine and Surgery, Maharajah's Institute of Medical Sciences, Vizianagaram, IND
| | - Afif Ramadhan
- Medicine, Universal Scientific Education and Research Network (USERN), Yogyakarta, IDN
- Medicine, Faculty of Medicine, Public Health, and Nursing, Gadjah Mada University, Yogyakarta, IDN
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15
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Caccamo M, Harrell FE, Hemnes AR. Evolution and optimization of clinical trial endpoints and design in pulmonary arterial hypertension. Pulm Circ 2023; 13:e12271. [PMID: 37554146 PMCID: PMC10405062 DOI: 10.1002/pul2.12271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/20/2023] [Accepted: 07/25/2023] [Indexed: 08/10/2023] Open
Abstract
Selection of endpoints for clinical trials in pulmonary arterial hypertension (PAH) is challenging because of the small numbers of patients and the changing expectations of patients, clinicians, and regulators in this evolving therapy area. The most commonly used primary endpoint in PAH trials has been 6-min walk distance (6MWD), leading to the approval of several targeted therapies. However, single surrogate endpoints such as 6MWD or hemodynamic parameters may not correlate with clinical outcomes. Composite endpoints of clinical worsening have been developed to reflect patients' overall condition more accurately, although there is no standard definition of worsening. Recently there has been a shift to composite endpoints assessing clinical improvement, and risk scores developed from registry data are increasingly being used. Biomarkers are another area of interest, although brain natriuretic peptide and its N-terminal prohormone are the only markers used for risk assessment or as endpoints in PAH. A range of other genetic, metabolic, and immunologic markers is currently under investigation, along with conventional and novel imaging modalities. Patient-reported outcomes are an increasingly important part of evaluating new therapies, and several PAH-specific tools are now available. In the future, alternative statistical techniques and trial designs, such as patient enrichment strategies, will play a role in evaluating PAH-targeted therapies. In addition, modern sequencing techniques, imaging analyses, and high-dimensional statistical modeling/machine learning may reveal novel markers that can play a role in the diagnosis and monitoring of PAH.
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Affiliation(s)
- Marco Caccamo
- Division of CardiologyWVU Heart and Vascular InstituteMorgantownWest VirginiaUSA
| | - Frank E. Harrell
- Department of BiostatisticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Anna R. Hemnes
- Division of Allergy, Pulmonary, and Critical Care MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
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16
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Dawes TJW, McCabe C, Dimopoulos K, Stewart I, Bax S, Harries C, Samaranayake CB, Kempny A, Molyneaux PL, Seitler S, Semple T, Li W, George PM, Kouranos V, Chua F, Renzoni EA, Kokosi M, Jenkins G, Wells AU, Wort SJ, Price LC. Phosphodiesterase 5 inhibitor treatment and survival in interstitial lung disease pulmonary hypertension: A Bayesian retrospective observational cohort study. Respirology 2023; 28:262-272. [PMID: 36172951 DOI: 10.1111/resp.14378] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 09/08/2022] [Indexed: 12/22/2022]
Abstract
BACKGROUND AND OBJECTIVE Pulmonary hypertension is a life-limiting complication of interstitial lung disease (ILD-PH). We investigated whether treatment with phosphodiesterase 5 inhibitors (PDE5i) in patients with ILD-PH was associated with improved survival. METHODS Consecutive incident patients with ILD-PH and right heart catheterisation, echocardiography and spirometry data were followed from diagnosis to death, transplantation or censoring with all follow-up and survival data modelled by Bayesian methods. RESULTS The diagnoses in 128 patients were idiopathic pulmonary fibrosis (n = 74, 58%), hypersensitivity pneumonitis (n = 17, 13%), non-specific interstitial pneumonia (n = 12, 9%), undifferentiated ILD (n = 8, 6%) and other lung diseases (n = 17, 13%). Final outcomes were death (n = 106, 83%), transplantation (n = 9, 7%) and censoring (n = 13, 10%). Patients treated with PDE5i (n = 50, 39%) had higher mean pulmonary artery pressure (median 38 mm Hg [interquartile range, IQR: 34, 43] vs. 35 mm Hg [IQR: 31, 38], p = 0.07) and percentage predicted forced vital capacity (FVC; median 57% [IQR: 51, 73] vs. 52% [IQR: 45, 66], p=0.08) though differences did not reach significance. Patients treated with PDE5i survived longer than untreated patients (median 2.18 years [95% CI: 1.43, 3.04] vs. 0.94 years [0.69, 1.51], p = 0.003) independent of all other prognostic markers by Bayesian joint-modelling (HR 0.39, 95% CI: 0.23, 0.59, p < 0.001) and propensity-matched analyses (HR 0.38, 95% CI: 0.22, 0.58, p < 0.001). Survival difference with treatment was significantly larger if right ventricular function was normal, rather than abnormal, at presentation (+2.55 years, 95% CI: -0.03, +3.97 vs. +0.98 years, 95% CI: +0.47, +2.00, p = 0.04). CONCLUSION PDE5i treatment in ILD-PH should be investigated by a prospective randomized trial.
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Affiliation(s)
- Timothy J W Dawes
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Colm McCabe
- National Heart and Lung Institute, Imperial College London, London, UK.,National Pulmonary Hypertension Service, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Konstantinos Dimopoulos
- National Heart and Lung Institute, Imperial College London, London, UK.,National Pulmonary Hypertension Service, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK.,Adult Congenital Heart Disease Service, Royal Brompton Hospital, London, UK
| | - Iain Stewart
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Simon Bax
- National Pulmonary Hypertension Service, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Carl Harries
- National Pulmonary Hypertension Service, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | - Aleksander Kempny
- National Heart and Lung Institute, Imperial College London, London, UK.,National Pulmonary Hypertension Service, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK.,Adult Congenital Heart Disease Service, Royal Brompton Hospital, London, UK
| | - Philip L Molyneaux
- National Heart and Lung Institute, Imperial College London, London, UK.,Department of Interstitial Lung Disease, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Samuel Seitler
- National Pulmonary Hypertension Service, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Thomas Semple
- National Heart and Lung Institute, Imperial College London, London, UK.,Department of Radiology, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Wei Li
- National Heart and Lung Institute, Imperial College London, London, UK.,Adult Congenital Heart Disease Service, Royal Brompton Hospital, London, UK.,Department of Echocardiography, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Peter M George
- National Heart and Lung Institute, Imperial College London, London, UK.,Department of Interstitial Lung Disease, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Vasileios Kouranos
- National Heart and Lung Institute, Imperial College London, London, UK.,Department of Interstitial Lung Disease, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Felix Chua
- National Heart and Lung Institute, Imperial College London, London, UK.,Department of Interstitial Lung Disease, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Elisabetta A Renzoni
- National Heart and Lung Institute, Imperial College London, London, UK.,Department of Interstitial Lung Disease, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Maria Kokosi
- National Heart and Lung Institute, Imperial College London, London, UK.,Department of Interstitial Lung Disease, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Gisli Jenkins
- National Heart and Lung Institute, Imperial College London, London, UK.,Department of Interstitial Lung Disease, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Athol U Wells
- National Heart and Lung Institute, Imperial College London, London, UK.,Department of Interstitial Lung Disease, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Stephen J Wort
- National Heart and Lung Institute, Imperial College London, London, UK.,National Pulmonary Hypertension Service, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Laura C Price
- National Heart and Lung Institute, Imperial College London, London, UK.,National Pulmonary Hypertension Service, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
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17
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Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease. Med Sci (Basel) 2023; 11:medsci11010020. [PMID: 36976528 PMCID: PMC10053913 DOI: 10.3390/medsci11010020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/20/2023] [Accepted: 02/22/2023] [Indexed: 03/02/2023] Open
Abstract
Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide, and it is associated with considerable economic burden. In an ageing, multimorbid population, it has become increasingly important to develop reliable, consistent, low-risk, non-invasive means of diagnosing CAD. The evolution of multiple cardiac modalities in this field has addressed this dilemma to a large extent, not only in providing information regarding anatomical disease, as is the case with coronary computed tomography angiography (CCTA), but also in contributing critical details about functional assessment, for instance, using stress cardiac magnetic resonance (S-CMR). The field of artificial intelligence (AI) is developing at an astounding pace, especially in healthcare. In healthcare, key milestones have been achieved using AI and machine learning (ML) in various clinical settings, from smartwatches detecting arrhythmias to retinal image analysis and skin cancer prediction. In recent times, we have seen an emerging interest in developing AI-based technology in the field of cardiovascular imaging, as it is felt that ML methods have potential to overcome some limitations of current risk models by applying computer algorithms to large databases with multidimensional variables, thus enabling the inclusion of complex relationships to predict outcomes. In this paper, we review the current literature on the various applications of AI in the assessment of CAD, with a focus on multimodality imaging, followed by a discussion on future perspectives and critical challenges that this field is likely to encounter as it continues to evolve in cardiology.
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18
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Artificial Intelligence in Cardiovascular CT and MR Imaging. Life (Basel) 2023; 13:life13020507. [PMID: 36836864 PMCID: PMC9968221 DOI: 10.3390/life13020507] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 02/06/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023] Open
Abstract
The technological development of Artificial Intelligence (AI) has grown rapidly in recent years. The applications of AI to cardiovascular imaging are various and could improve the radiologists' workflow, speeding up acquisition and post-processing time, increasing image quality and diagnostic accuracy. Several studies have already proved AI applications in Coronary Computed Tomography Angiography and Cardiac Magnetic Resonance, including automatic evaluation of calcium score, quantification of coronary stenosis and plaque analysis, or the automatic quantification of heart volumes and myocardial tissue characterization. The aim of this review is to summarize the latest advances in the field of AI applied to cardiovascular CT and MR imaging.
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19
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Iyer K, Morris A, Zenger B, Karanth K, Khan N, Orkild BA, Korshak O, Elhabian S. Statistical shape modeling of multi-organ anatomies with shared boundaries. Front Bioeng Biotechnol 2023; 10:1078800. [PMID: 36727040 PMCID: PMC9886138 DOI: 10.3389/fbioe.2022.1078800] [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] [Received: 10/24/2022] [Accepted: 12/28/2022] [Indexed: 01/13/2023] Open
Abstract
Introduction: Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into some quantitative representation (such as correspondence points or landmarks) which can be used to study the covariance patterns of the shapes and answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is a four-chambered organ with several shared boundaries between chambers. Subtle shape changes within the shared boundaries of the heart can indicate potential pathologic changes such as right ventricular overload. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM methods do not explicitly handle shared boundaries which aid in a better understanding of the anatomy of interest. If shared boundaries are not explicitly modeled, it restricts the capability of the shape model to identify the pathological shape changes occurring at the shared boundary. Hence, this paper presents a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that explicitly model contact surfaces. Methods: This work focuses on particle-based shape modeling (PSM), a state-of-art SSM approach for building shape models by optimizing the position of correspondence particles. The proposed PSM strategy for handling shared boundaries entails (a) detecting and extracting the shared boundary surface and contour (outline of the surface mesh/isoline) of the meshes of the two organs, (b) followed by a formulation for a correspondence-based optimization algorithm to build a multi-organ anatomy statistical shape model that captures morphological and alignment changes of individual organs and their shared boundary surfaces throughout the population. Results: We demonstrate the shared boundary pipeline using a toy dataset of parameterized shapes and a clinical dataset of the biventricular heart models. The shared boundary model for the cardiac biventricular data achieves consistent parameterization of the shared surface (interventricular septum) and identifies the curvature of the interventricular septum as pathological shape differences.
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Affiliation(s)
- Krithika Iyer
- University of Utah, School of Computing, Salt Lake City, UT, United States
- University of Utah, Scientific Computing and Imaging Institute, Salt Lake City, UT, United States
| | - Alan Morris
- University of Utah, Scientific Computing and Imaging Institute, Salt Lake City, UT, United States
| | - Brian Zenger
- University of Utah, Scientific Computing and Imaging Institute, Salt Lake City, UT, United States
- University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Karthik Karanth
- University of Utah, School of Computing, Salt Lake City, UT, United States
- University of Utah, Scientific Computing and Imaging Institute, Salt Lake City, UT, United States
| | - Nawazish Khan
- University of Utah, School of Computing, Salt Lake City, UT, United States
- University of Utah, Scientific Computing and Imaging Institute, Salt Lake City, UT, United States
| | - Benjamin A. Orkild
- University of Utah, Scientific Computing and Imaging Institute, Salt Lake City, UT, United States
- University of Utah, Department of Biomedical Engineering, Salt Lake City, UT, United States
| | - Oleksandre Korshak
- University of Utah, School of Computing, Salt Lake City, UT, United States
- University of Utah, Scientific Computing and Imaging Institute, Salt Lake City, UT, United States
| | - Shireen Elhabian
- University of Utah, School of Computing, Salt Lake City, UT, United States
- University of Utah, Scientific Computing and Imaging Institute, Salt Lake City, UT, United States
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20
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Kline A, Wang H, Li Y, Dennis S, Hutch M, Xu Z, Wang F, Cheng F, Luo Y. Multimodal machine learning in precision health: A scoping review. NPJ Digit Med 2022; 5:171. [PMID: 36344814 PMCID: PMC9640667 DOI: 10.1038/s41746-022-00712-8] [Citation(s) in RCA: 74] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022] Open
Abstract
Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.
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Affiliation(s)
- Adrienne Kline
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Hanyin Wang
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Yikuan Li
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Saya Dennis
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Meghan Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Cornell University, New York, 10065, NY, USA
| | - Fei Wang
- Department of Population Health Sciences, Cornell University, New York, 10065, NY, USA
| | - Feixiong Cheng
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, 44195, OH, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA.
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21
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Rau A, Soschynski M, Taron J, Ruile P, Schlett CL, Bamberg F, Krauss T. [Artificial intelligence and radiomics : Value in cardiac MRI]. RADIOLOGIE (HEIDELBERG, GERMANY) 2022; 62:947-953. [PMID: 36006439 DOI: 10.1007/s00117-022-01060-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/22/2022] [Indexed: 06/15/2023]
Abstract
CLINICAL/METHODICAL ISSUE Cardiac diseases are the leading cause of death. Many diseases can be specifically treated once a valid diagnosis is established. Cardiac magnetic resonance imaging (MRI) plays a central role in the workup of many cardiac pathologies. However, image acquisition as well as interpretation and related secondary image evaluation are time-consuming and complex. STANDARD RADIOLOGICAL METHODS Cardiac MRI is becoming increasingly established in international guidelines for the evaluation of cardiac function and differential diagnosis of a wide variety of cardiac diseases. METHODOLOGICAL INNOVATIONS Cardiac MRI has limited reproducibility due to the acquisition technique and interpretation of findings with complex secondary measurements. Artificial intelligence techniques and radiomics offer the potential to improve the acquisition, interpretation, and reproducibility of cardiac MRI. PERFORMANCE Research suggests that artificial intelligence and radiomic analysis can improve cardiac MRI in terms of image acquisition and also diagnostic and prognostic value. Furthermore, the implementation of artificial intelligence and radiomics may result in the identification of new biomarkers. ACHIEVEMENTS AND PRACTICAL RECOMMENDATIONS The implementation of artificial intelligence in cardiac MRI has great potential. However, the current level of evidence is still limited in some aspects; in particular there are too few prospective and large multicenter studies available. As a result, the algorithms developed are often not sufficiently validated scientifically and are not yet applied in clinical routine.
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Affiliation(s)
- Alexander Rau
- Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland.
| | - Martin Soschynski
- Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Jana Taron
- Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Philipp Ruile
- Klinik für Klinik für Kardiologie und Angiologie, Universitäts-Herzzentrum Freiburg - Bad Krozingen, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Bad Krozingen, Deutschland
| | - Christopher L Schlett
- Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Fabian Bamberg
- Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
| | - Tobias Krauss
- Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland
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22
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Velasco C, Fletcher TJ, Botnar RM, Prieto C. Artificial intelligence in cardiac magnetic resonance fingerprinting. Front Cardiovasc Med 2022; 9:1009131. [PMID: 36204566 PMCID: PMC9530662 DOI: 10.3389/fcvm.2022.1009131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T1 and T2 mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.
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Affiliation(s)
- Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- *Correspondence: Carlos Velasco
| | - Thomas J. Fletcher
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - René M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
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23
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Assadi H, Alabed S, Maiter A, Salehi M, Li R, Ripley DP, Van der Geest RJ, Zhong Y, Zhong L, Swift AJ, Garg P. The Role of Artificial Intelligence in Predicting Outcomes by Cardiovascular Magnetic Resonance: A Comprehensive Systematic Review. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:1087. [PMID: 36013554 PMCID: PMC9412853 DOI: 10.3390/medicina58081087] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/28/2022] [Accepted: 08/06/2022] [Indexed: 11/16/2022]
Abstract
Background and Objectives: Interest in artificial intelligence (AI) for outcome prediction has grown substantially in recent years. However, the prognostic role of AI using advanced cardiac magnetic resonance imaging (CMR) remains unclear. This systematic review assesses the existing literature on AI in CMR to predict outcomes in patients with cardiovascular disease. Materials and Methods: Medline and Embase were searched for studies published up to November 2021. Any study assessing outcome prediction using AI in CMR in patients with cardiovascular disease was eligible for inclusion. All studies were assessed for compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results: A total of 5 studies were included, with a total of 3679 patients, with 225 deaths and 265 major adverse cardiovascular events. Three methods demonstrated high prognostic accuracy: (1) three-dimensional motion assessment model in pulmonary hypertension (hazard ratio (HR) 2.74, 95%CI 1.73−4.34, p < 0.001), (2) automated perfusion quantification in patients with coronary artery disease (HR 2.14, 95%CI 1.58−2.90, p < 0.001), and (3) automated volumetric, functional, and area assessment in patients with myocardial infarction (HR 0.94, 95%CI 0.92−0.96, p < 0.001). Conclusion: There is emerging evidence of the prognostic role of AI in predicting outcomes for three-dimensional motion assessment in pulmonary hypertension, ischaemia assessment by automated perfusion quantification, and automated functional assessment in myocardial infarction.
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Affiliation(s)
- Hosamadin Assadi
- Department of Medicine, Norwich Medical School, University of East Anglia, Norfolk NR4 7TJ, UK
- Department of Cardiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK
| | - Samer Alabed
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
| | - Ahmed Maiter
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
| | - Mahan Salehi
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
| | - Rui Li
- Department of Medicine, Norwich Medical School, University of East Anglia, Norfolk NR4 7TJ, UK
- Department of Cardiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK
| | - David P. Ripley
- Northumbria Healthcare Foundation Trust, Northumbria Specialist Care Emergency Hospital, Northumbria Way, Northumberland NE23 6NZ, UK
| | - Rob J. Van der Geest
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Yumin Zhong
- Department of Radiology, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, 1678 Dong Fang Rd., Shanghai 200127, China
| | - Liang Zhong
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore 169609, Singapore
- Cardiovascular Sciences, Duke-NUS Medical School, 8 College Road, Singapore 169856, Singapore
| | - Andrew J. Swift
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield S10 2RX, UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
| | - Pankaj Garg
- Department of Medicine, Norwich Medical School, University of East Anglia, Norfolk NR4 7TJ, UK
- Department of Cardiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk NR4 7UY, UK
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24
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Orkild BA, Zenger B, Iyer K, Rupp LC, Ibrahim MM, Khashani AG, Perez MD, Foote MD, Bergquist JA, Morris AK, Kim JJ, Steinberg BA, Selzman C, Ratcliffe MB, MacLeod RS, Elhabian S, Morgan AE. All Roads Lead to Rome: Diverse Etiologies of Tricuspid Regurgitation Create a Predictable Constellation of Right Ventricular Shape Changes. Front Physiol 2022; 13:908552. [PMID: 35860653 PMCID: PMC9291517 DOI: 10.3389/fphys.2022.908552] [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: 03/30/2022] [Accepted: 05/16/2022] [Indexed: 11/17/2022] Open
Abstract
Introduction: Myriad disorders cause right ventricular (RV) dilation and lead to tricuspid regurgitation (TR). Because the thin-walled, flexible RV is mechanically coupled to the pulmonary circulation and the left ventricular septum, it distorts with any disturbance in the cardiopulmonary system. TR, therefore, can result from pulmonary hypertension, left heart failure, or intrinsic RV dysfunction; but once it occurs, TR initiates a cycle of worsening RV volume overload, potentially progressing to right heart failure. Characteristic three-dimensional RV shape-changes from this process, and changes particular to individual TR causes, have not been defined in detail. Methods: Cardiac MRI was obtained in 6 healthy volunteers, 41 patients with ≥ moderate TR, and 31 control patients with cardiac disease without TR. The mean shape of each group was constructed using a three-dimensional statistical shape model via the particle-based shape modeling approach. Changes in shape were examined across pulmonary hypertension and congestive heart failure subgroups using principal component analysis (PCA). A logistic regression approach based on these PCA modes identified patients with TR using RV shape alone. Results: Mean RV shape in patients with TR exhibited free wall bulging, narrowing of the base, and blunting of the RV apex compared to controls (p < 0.05). Using four primary PCA modes, a logistic regression algorithm identified patients with TR correctly with 82% recall and 87% precision. In patients with pulmonary hypertension without TR, RV shape was narrower and more streamlined than in healthy volunteers. However, in RVs with TR and pulmonary hypertension, overall RV shape continued to demonstrate the free wall bulging characteristic of TR. In the subgroup of patients with congestive heart failure without TR, this intermediate state of RV muscular hypertrophy was not present. Conclusion: The multiple causes of TR examined in this study changed RV shape in similar ways. Logistic regression classification based on these shape changes reliably identified patients with TR regardless of etiology. Furthermore, pulmonary hypertension without TR had unique shape features, described here as the "well compensated" RV. These results suggest shape modeling as a promising tool for defining severity of RV disease and risk of decompensation, particularly in patients with pulmonary hypertension.
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Affiliation(s)
- Benjamin A. Orkild
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
| | - Brian Zenger
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
| | - Krithika Iyer
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
- School of Computing, University of Utah, Salt Lake City, UT, United States
| | - Lindsay C. Rupp
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
| | - Majd M Ibrahim
- Division of Cardiovascular Medicine, University of Utah, Salt Lake City, UT, United States
| | - Atefeh G. Khashani
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
| | - Maura D. Perez
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
| | - Markus D. Foote
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
| | - Jake A. Bergquist
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
| | - Alan K. Morris
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
| | - Jiwon J. Kim
- Weill-Cornell Medical College, Division of Cardiology, New York, NY, United States
| | - Benjamin A. Steinberg
- Division of Cardiovascular Medicine, University of Utah, Salt Lake City, UT, United States
| | - Craig Selzman
- Division of Cardiothoracic Surgery, University of Utah, Salt Lake City, UT, United States
| | - Mark B. Ratcliffe
- Department of Surgery, The San Francisco VA Medical Center, University of California, San Francisco, San Francisco, CA, United States
| | - Rob S. MacLeod
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
| | - Shireen Elhabian
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
- School of Computing, University of Utah, Salt Lake City, UT, United States
| | - Ashley E. Morgan
- St. Luke’s Medical Center Cardiothoracic and Vascular Surgery, Boise, ID, United States
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25
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Qin C, Murali S, Lee E, Supramaniam V, Hausenloy DJ, Obungoloch J, Brecher J, Lin R, Ding H, Akudjedu TN, Anazodo UC, Jagannathan NR, Ntusi NAB, Simonetti OP, Campbell-Washburn AE, Niendorf T, Mammen R, Adeleke S. Sustainable low-field cardiovascular magnetic resonance in changing healthcare systems. Eur Heart J Cardiovasc Imaging 2022; 23:e246-e260. [PMID: 35157038 PMCID: PMC9159744 DOI: 10.1093/ehjci/jeab286] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/14/2021] [Indexed: 11/14/2022] Open
Abstract
Cardiovascular disease continues to be a major burden facing healthcare systems worldwide. In the developed world, cardiovascular magnetic resonance (CMR) is a well-established non-invasive imaging modality in the diagnosis of cardiovascular disease. However, there is significant global inequality in availability and access to CMR due to its high cost, technical demands as well as existing disparities in healthcare and technical infrastructures across high-income and low-income countries. Recent renewed interest in low-field CMR has been spurred by the clinical need to provide sustainable imaging technology capable of yielding diagnosticquality images whilst also being tailored to the local populations and healthcare ecosystems. This review aims to evaluate the technical, practical and cost considerations of low field CMR whilst also exploring the key barriers to implementing sustainable MRI in both the developing and developed world.
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Affiliation(s)
- Cathy Qin
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
| | - Sanjana Murali
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
| | - Elsa Lee
- School of Medicine, Faculty of Medicine, Imperial College London, London, UK
| | | | - Derek J Hausenloy
- Division of Medicine, University College London, London, UK
- Cardiovascular & Metabolic Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
- National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University Singapore, Singapore, Singapore
- Hatter Cardiovascular Institue, UCL Institute of Cardiovascular Sciences, University College London, London, UK
- Cardiovascular Research Center, College of Medical and Health Sciences, Asia University, Taichung, Taiwan
| | - Johnes Obungoloch
- Department of Biomedical Engineering, Mbarara University of Science and Technology, Mbarara, Uganda
| | | | - Rongyu Lin
- School of Medicine, University College London, London, UK
| | - Hao Ding
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
| | - Theophilus N Akudjedu
- Institute of Medical Imaging and Visualisation, Faculty of Health and Social Science, Bournemouth University, Poole, UK
| | | | - Naranamangalam R Jagannathan
- Department of Electrical Engineering, Indian Institute of Technology, Chennai, India
- Department of Radiology, Sri Ramachandra University Medical College, Chennai, India
- Department of Radiology, Chettinad Hospital and Research Institute, Kelambakkam, India
| | - Ntobeko A B Ntusi
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, Western Cape, South Africa
| | - Orlando P Simonetti
- Division of Cardiovascular Medicine, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA
- Department of Radiology, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Adrienne E Campbell-Washburn
- Cardiovascular Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück Centre for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Regina Mammen
- Department of Cardiology, The Essex Cardiothoracic Centre, Basildon, UK
| | - Sola Adeleke
- School of Cancer & Pharmaceutical Sciences, King’s College London, Queen Square, London WC1N 3BG, UK
- High Dimensional Neurology, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
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26
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Zhou Y, Panda A, Jokerst CE. Editorial for "Evaluation of Pulmonary Hypertension Using 4D Flow MRI". J Magn Reson Imaging 2022; 56:246-247. [PMID: 35567592 DOI: 10.1002/jmri.28235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 11/11/2022] Open
Affiliation(s)
- Yuxiang Zhou
- Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Anshuman Panda
- Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona, USA
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27
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Alabed S, Uthoff J, Zhou S, Garg P, Dwivedi K, Alandejani F, Gosling R, Schobs L, Brook M, Shahin Y, Capener D, Johns CS, Wild JM, Rothman AMK, van der Geest RJ, Condliffe R, Kiely DG, Lu H, Swift AJ. Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:265-275. [PMID: 36713008 PMCID: PMC9708011 DOI: 10.1093/ehjdh/ztac022] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 02/19/2022] [Indexed: 02/01/2023]
Abstract
Aims Pulmonary arterial hypertension (PAH) is a rare but serious disease associated with high mortality if left untreated. This study aims to assess the prognostic cardiac magnetic resonance (CMR) features in PAH using machine learning. Methods and results Seven hundred and twenty-three consecutive treatment-naive PAH patients were identified from the ASPIRE registry; 516 were included in the training, and 207 in the validation cohort. A multilinear principal component analysis (MPCA)-based machine learning approach was used to extract mortality and survival features throughout the cardiac cycle. The features were overlaid on the original imaging using thresholding and clustering of high- and low-risk of mortality prediction values. The 1-year mortality rate in the validation cohort was 10%. Univariable Cox regression analysis of the combined short-axis and four-chamber MPCA-based predictions was statistically significant (hazard ratios: 2.1, 95% CI: 1.3, 3.4, c-index = 0.70, P = 0.002). The MPCA features improved the 1-year mortality prediction of REVEAL from c-index = 0.71 to 0.76 (P ≤ 0.001). Abnormalities in the end-systolic interventricular septum and end-diastolic left ventricle indicated the highest risk of mortality. Conclusion The MPCA-based machine learning is an explainable time-resolved approach that allows visualization of prognostic cardiac features throughout the cardiac cycle at the population level, making this approach transparent and clinically interpretable. In addition, the added prognostic value over the REVEAL risk score and CMR volumetric measurements allows for a more accurate prediction of 1-year mortality risk in PAH.
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Affiliation(s)
| | - Johanna Uthoff
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Shuo Zhou
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Pankaj Garg
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Krit Dwivedi
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK,Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Faisal Alandejani
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Rebecca Gosling
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Lawrence Schobs
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Martin Brook
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Yousef Shahin
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Dave Capener
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Christopher S Johns
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK,Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Jim M Wild
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK,INSIGNEO, Institute for in silico medicine, University of Sheffield, UK
| | - Alexander M K Rothman
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | | | - Robin Condliffe
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK,Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
| | - David G Kiely
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK,INSIGNEO, Institute for in silico medicine, University of Sheffield, UK,Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
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28
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Rhodes CJ, Sweatt AJ, Maron BA. Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension. Circ Res 2022; 130:1423-1444. [PMID: 35482840 PMCID: PMC9070103 DOI: 10.1161/circresaha.121.319969] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Pulmonary hypertension is a complex disease with multiple causes, corresponding to phenotypic heterogeneity and variable therapeutic responses. Advancing understanding of pulmonary hypertension pathogenesis is likely to hinge on integrated methods that leverage data from health records, imaging, novel molecular -omics profiling, and other modalities. In this review, we summarize key data sets generated thus far in the field and describe analytical methods that hold promise for deciphering the molecular mechanisms that underpin pulmonary vascular remodeling, including machine learning, network medicine, and functional genetics. We also detail how genetic and subphenotyping approaches enable earlier diagnosis, refined prognostication, and optimized treatment prediction. We propose strategies that identify functionally important molecular pathways, bolstered by findings across multi-omics platforms, which are well-positioned to individualize drug therapy selection and advance precision medicine in this highly morbid disease.
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Affiliation(s)
- Christopher J Rhodes
- Department of Medicine, National Heart and Lung Institute, Imperial College London, United Kingdom (C.J.R.)
| | - Andrew J Sweatt
- Department of Medicine, National Heart and Lung Institute, Imperial College London, United Kingdom (C.J.R.)
| | - Bradley A Maron
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (B.A.M.).,Division of Cardiology, VA Boston Healthcare System, West Roxbury, MA (B.A.M.)
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29
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Wang S, Chauhan D, Patel H, Amir-Khalili A, da Silva IF, Sojoudi A, Friedrich S, Singh A, Landeras L, Miller T, Ameyaw K, Narang A, Kawaji K, Tang Q, Mor-Avi V, Patel AR. Assessment of right ventricular size and function from cardiovascular magnetic resonance images using artificial intelligence. J Cardiovasc Magn Reson 2022; 24:27. [PMID: 35410226 PMCID: PMC8996592 DOI: 10.1186/s12968-022-00861-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/29/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, despite the complex RV geometry. However, in our recent study, commercially available deep learning (DL) algorithms for RVEF quantification performed poorly in some patients. The current study was designed to test the hypothesis that quantification of RV function could be improved in these patients by using more diverse CMR datasets in addition to domain-specific quantitative performance evaluation metrics during the cross-validation phase of DL algorithm development. METHODS We identified 100 patients from our prior study who had the largest differences between manually measured and automated RVEF values. Automated RVEF measurements were performed using the original version of the algorithm (DL1), an updated version (DL2) developed from a dataset that included a wider range of RV pathology and validated using multiple domain-specific quantitative performance evaluation metrics, and conventional methodology performed by a core laboratory (CORE). Each of the DL-RVEF approaches was compared against CORE-RVEF reference values using linear regression and Bland-Altman analyses. Additionally, RVEF values were classified into 3 categories: ≤ 35%, 35-50%, and ≥ 50%. Agreement between RVEF classifications made by the DL approaches and the CORE measurements was tested. RESULTS CORE-RVEF and DL-RVEFs were obtained in all patients (feasibility of 100%). DL2-RVEF correlated with CORE-RVEF better than DL1-RVEF (r = 0.87 vs. r = 0.42), with narrower limits of agreement. As a result, DL2 algorithm also showed increasing accuracy from 0.53 to 0.80 for categorizing RV function. CONCLUSIONS The use of a new DL algorithm cross-validated on a dataset with a wide range of RV pathology using multiple domain-specific metrics resulted in a considerable improvement in the accuracy of automated RVEF measurements. This improvement was demonstrated in patients whose images were the most challenging and resulted in the largest RVEF errors. These findings underscore the critical importance of this strategy in the development of DL approaches for automated CMR measurements.
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Affiliation(s)
- Shuo Wang
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
- Peking University Shougang Hospital, Beijing, China
| | - Daksh Chauhan
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | - Hena Patel
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | | | | | | | | | - Amita Singh
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | - Luis Landeras
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Tamari Miller
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | - Keith Ameyaw
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | | | - Keigo Kawaji
- Illinois Institute of Technology, Chicago, IL, USA
| | - Qiang Tang
- Peking University Shougang Hospital, Beijing, China
| | - Victor Mor-Avi
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA
| | - Amit R Patel
- Department of Medicine, University of Chicago, University of Chicago Medical Center, 5758 S. Maryland Avenue, Chicago, IL, MC906760637, USA.
- Department of Radiology, University of Chicago, Chicago, IL, USA.
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Xu J, Desmond EL, Wong TC, Neill CG, Simon MA, Brigham JC. Right Ventricular Shape Feature Quantification for Evaluation of Pulmonary Hypertension: Feasibility and Preliminary Associations With Clinical Outcome Submitted for Publication. J Biomech Eng 2022; 144:1120496. [PMID: 34549255 DOI: 10.1115/1.4052495] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Indexed: 11/08/2022]
Abstract
This study aimed to demonstrate feasibility of statistical shape analysis techniques to identify distinguishing features of right ventricle (RV) shape as related to hemodynamic variables and outcome data in pulmonary hypertension (PH). Cardiovascular magnetic resonance images were acquired from 50 patients (33 PH, 17 non-PH). Contemporaneous right heart catheterization data were collected for all individuals. Outcome was defined by all-cause mortality and hospitalization for heart failure. RV endocardial borders were manually segmented, and three-dimensional surfaces reconstructed at end diastole and end systole. Registration and harmonic mapping were then used to create a quantitative correspondence between all RV surfaces. Proper orthogonal decomposition was performed to generate modes describing RV shape features. The first 15 modes captured over 98% of the total modal energy. Two shape modes, 8 (free wall expansion) and 13 (septal flattening), stood out as relating to PH state (mode 13: r = 0.424, p = 0.002; mode 8: r = 0.429, p = 0.002). Mode 13 was significantly correlated with outcome (r = 0.438, p = 0.001), more so than any hemodynamic variable. Shape analysis techniques can derive unique RV shape descriptors corresponding to specific, anatomically meaningful features. The modes quantify shape features that had been previously only qualitatively related to PH progression. Modes describing relevant RV features are shown to correlate with clinical measures of RV status, as well as outcomes. These new shape descriptors lay the groundwork for a noninvasive strategy for identification of failing RVs, beyond what is currently available to clinicians.
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Affiliation(s)
- Jing Xu
- Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 15261
| | | | - Timothy C Wong
- Department of Medicine, Division of Cardiology, University of Pittsburgh School of Medicine Cardiovascular Magnetic Resonance Center, UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, PA 15213
| | - Colin G Neill
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37240
| | - Marc A Simon
- Department of Medicine/Division of Cardiology, University of California, San Francisco, CA 94143
| | - John C Brigham
- Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 15261
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Saunders LC, Hughes PJC, Alabed S, Capener DJ, Marshall H, Vogel-Claussen J, van Beek EJR, Kiely DG, Swift AJ, Wild JM. Integrated Cardiopulmonary MRI Assessment of Pulmonary Hypertension. J Magn Reson Imaging 2022; 55:633-652. [PMID: 34350655 DOI: 10.1002/jmri.27849] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/21/2021] [Accepted: 05/24/2021] [Indexed: 11/12/2022] Open
Abstract
Pulmonary hypertension (PH) is a heterogeneous condition that can affect the lung parenchyma, pulmonary vasculature, and cardiac chambers. Accurate diagnosis often requires multiple complex assessments of the cardiac and pulmonary systems. MRI is able to comprehensively assess cardiac structure and function, as well as lung parenchymal, pulmonary vascular, and functional lung changes. Therefore, MRI has the potential to provide an integrated functional and structural assessment of the cardiopulmonary system in a single exam. Cardiac MRI is used in the assessment of PH in most large PH centers, whereas lung MRI is an emerging technique in patients with PH. This article reviews the current literature on cardiopulmonary MRI in PH, including cine MRI, black-blood imaging, late gadolinium enhancement, T1 mapping, myocardial strain analysis, contrast-enhanced perfusion imaging and contrast-enhanced MR angiography, and hyperpolarized gas functional lung imaging. This article also highlights recent developments in this field and areas of interest for future research including cardiac MRI-based diagnostic models, machine learning in cardiac MRI, oxygen-enhanced 1 H imaging, contrast-free 1 H perfusion and ventilation imaging, contrast-free angiography and UTE imaging. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Laura C Saunders
- Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Paul J C Hughes
- Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Samer Alabed
- Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | | | - Helen Marshall
- Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Jens Vogel-Claussen
- Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | | | - David G Kiely
- Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Andrew J Swift
- Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.,Imaging, Sheffield Teaching Hospitals, Sheffield, UK
| | - Jim M Wild
- Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
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Wang S, Patel H, Miller T, Ameyaw K, Narang A, Chauhan D, Anand S, Anyanwu E, Besser SA, Kawaji K, Liu XP, Lang RM, Mor-Avi V, Patel AR. AI Based CMR Assessment of Biventricular Function: Clinical Significance of Intervendor Variability and Measurement Errors. JACC Cardiovasc Imaging 2022; 15:413-427. [PMID: 34656471 PMCID: PMC8917993 DOI: 10.1016/j.jcmg.2021.08.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 08/09/2021] [Accepted: 08/17/2021] [Indexed: 12/30/2022]
Abstract
OBJECTIVES The aim of this study was to determine whether left ventricular ejection fraction (LVEF) and right ventricular ejection fraction (RVEF) and left ventricular mass (LVM) measurements made using 3 fully automated deep learning (DL) algorithms are accurate and interchangeable and can be used to classify ventricular function and risk-stratify patients as accurately as an expert. BACKGROUND Artificial intelligence is increasingly used to assess cardiac function and LVM from cardiac magnetic resonance images. METHODS Two hundred patients were identified from a registry of individuals who underwent vasodilator stress cardiac magnetic resonance. LVEF, LVM, and RVEF were determined using 3 fully automated commercial DL algorithms and by a clinical expert (CLIN) using conventional methodology. Additionally, LVEF values were classified according to clinically important ranges: <35%, 35% to 50%, and ≥50%. Both ejection fraction values and classifications made by the DL ejection fraction approaches were compared against CLIN ejection fraction reference. Receiver-operating characteristic curve analysis was performed to evaluate the ability of CLIN and each of the DL classifications to predict major adverse cardiovascular events. RESULTS Excellent correlations were seen for each DL-LVEF compared with CLIN-LVEF (r = 0.83-0.93). Good correlations were present between DL-LVM and CLIN-LVM (r = 0.75-0.85). Modest correlations were observed between DL-RVEF and CLIN-RVEF (r = 0.59-0.68). A >10% error between CLIN and DL ejection fraction was present in 5% to 18% of cases for the left ventricle and 23% to 43% for the right ventricle. LVEF classification agreed with CLIN-LVEF classification in 86%, 80%, and 85% cases for the 3 DL-LVEF approaches. There were no differences among the 4 approaches in associations with major adverse cardiovascular events for LVEF, LVM, and RVEF. CONCLUSIONS This study revealed good agreement between automated and expert-derived LVEF and similarly strong associations with outcomes, compared with an expert. However, the ability of these automated measurements to accurately classify left ventricular function for treatment decision remains limited. DL-LVM showed good agreement with CLIN-LVM. DL-RVEF approaches need further refinements.
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Affiliation(s)
- Shuo Wang
- University of Chicago, Chicago, Illinois,Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Hena Patel
- University of Chicago, Chicago, Illinois
| | | | | | | | | | | | | | | | - Keigo Kawaji
- University of Chicago, Chicago, Illinois,Illinois Institute of Technology, Chicago, Illinois
| | - Xing-Peng Liu
- Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
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33
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Fotaki A, Puyol-Antón E, Chiribiri A, Botnar R, Pushparajah K, Prieto C. Artificial Intelligence in Cardiac MRI: Is Clinical Adoption Forthcoming? Front Cardiovasc Med 2022; 8:818765. [PMID: 35083303 PMCID: PMC8785419 DOI: 10.3389/fcvm.2021.818765] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 12/15/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) refers to the area of knowledge that develops computerised models to perform tasks that typically require human intelligence. These algorithms are programmed to learn and identify patterns from "training data," that can be subsequently applied to new datasets, without being explicitly programmed to do so. AI is revolutionising the field of medical imaging and in particular of Cardiovascular Magnetic Resonance (CMR) by providing deep learning solutions for image acquisition, reconstruction and analysis, ultimately supporting the clinical decision making. Numerous methods have been developed over recent years to enhance and expedite CMR data acquisition, image reconstruction, post-processing and analysis; along with the development of promising AI-based biomarkers for a wide spectrum of cardiac conditions. The exponential rise in the availability and complexity of CMR data has fostered the development of different AI models. Integration in clinical routine in a meaningful way remains a challenge. Currently, innovations in this field are still mostly presented in proof-of-concept studies with emphasis on the engineering solutions; often recruiting small patient cohorts or relying on standardised databases such as Multi-ethnic Study on atherosclerosis (MESA), UK Biobank and others. The wider incorporation of clinically valid endpoints such as symptoms, survival, need and response to treatment remains to be seen. This review briefly summarises the current principles of AI employed in CMR and explores the relevant prospective observational studies in cardiology patient cohorts. It provides an overview of clinical studies employing undersampled reconstruction techniques to speed up the scan encompassing cine imaging, whole-heart imaging, multi-parametric mapping and magnetic resonance fingerprinting along with the clinical utility of AI applications in image post-processing, and analysis. Specific focus is given to studies that have incorporated CMR-derived prediction models for prognostication in cardiac disease. It also discusses current limitations and proposes potential developments to enable multi-disciplinary collaboration for improved evidence-based medicine. AI is an extremely promising field and the timely integration of clinician's input in the ingenious technical investigator's paradigm holds promise for a bright future in the medical field.
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Affiliation(s)
- Anastasia Fotaki
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Esther Puyol-Antón
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Amedeo Chiribiri
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - René Botnar
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Kuberan Pushparajah
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Claudia Prieto
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Taylor AM. The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging. Pediatr Radiol 2022; 52:2131-2138. [PMID: 34936019 PMCID: PMC9537201 DOI: 10.1007/s00247-021-05218-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/13/2021] [Accepted: 10/05/2021] [Indexed: 11/24/2022]
Abstract
Artificial intelligence (AI) offers the potential to change many aspects of paediatric cardiac imaging. At present, there are only a few clinically validated examples of AI applications in this field. This review focuses on the use of AI in paediatric cardiovascular MRI, using examples from paediatric cardiovascular MRI, adult cardiovascular MRI and other radiologic experience.
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Affiliation(s)
- Andrew M. Taylor
- Great Ormond Street Hospital for Children, Zayed Centre for Research, 20 Guildford St., Room 3.7, London, WC1N 1DZ UK ,Cardiovascular Imaging, UCL Institute of Cardiovascular Science, London, UK
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Reducing the Heart Failure Burden in Romania by Predicting Congestive Heart Failure Using Artificial Intelligence: Proof of Concept. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112411728] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Due to population aging, we are currently confronted with an increased number of chronic heart failure patients. The primary purpose of this study was to implement a noncontact system that can predict heart failure exacerbation through vocal analysis. We designed the system to evaluate the voice characteristics of every patient, and we used the identified variations as an input for a machine-learning-based approach. We collected data from a total of 16 patients, 9 men and 7 women, aged 65–91 years old, who agreed to take part in the study, with a detailed signed informed consent. We included hospitalized patients admitted with cardiogenic acute pulmonary edema in the study, regardless of the precipitation cause or other known cardiovascular comorbidities. There were no specific exclusion criteria, except age (which had to be over 18 years old) and patients with speech inabilities. We then recorded each patient’s voice twice a day, using the same smartphone, Lenovo P780, from day one of hospitalization—when their general status was critical—until the day of discharge, when they were clinically stable. We used the New York Heart Association Functional Classification (NYHA) classification system for heart failure to include the patients in stages based on their clinical evolution. Each voice recording has been accordingly equated and subsequently introduced into the machine-learning algorithm. We used multiple machine-learning techniques for classification in order to detect which one turns out to be more appropriate for the given dataset and the one that can be the starting point for future developments. We used algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). After integrating the information from 15 patients, the algorithm correctly classified the 16th patient into the third NYHA stage at hospitalization and second NYHA stage at discharge, based only on his voice recording. The KNN algorithm proved to have the best classification accuracy, with a value of 0.945. Voice is a cheap and easy way to monitor a patient’s health status. The algorithm we have used for analyzing the voice provides highly accurate preliminary results. We aim to obtain larger datasets and compute more complex voice analyzer algorithms to certify the outcomes presented.
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Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine. J Clin Med 2021; 10:jcm10235710. [PMID: 34884412 PMCID: PMC8658222 DOI: 10.3390/jcm10235710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 12/02/2021] [Indexed: 12/21/2022] Open
Abstract
The future of healthcare is an organic blend of technology, innovation, and human connection. As artificial intelligence (AI) is gradually becoming a go-to technology in healthcare to improve efficiency and outcomes, we must understand our limitations. We should realize that our goal is not only to provide faster and more efficient care, but also to deliver an integrated solution to ensure that the care is fair and not biased to a group of sub-population. In this context, the field of cardio-cerebrovascular diseases, which encompasses a wide range of conditions-from heart failure to stroke-has made some advances to provide assistive tools to care providers. This article aimed to provide an overall thematic review of recent development focusing on various AI applications in cardio-cerebrovascular diseases to identify gaps and potential areas of improvement. If well designed, technological engines have the potential to improve healthcare access and equitability while reducing overall costs, diagnostic errors, and disparity in a system that affects patients and providers and strives for efficiency.
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37
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Callegarin D, Callier P. Enjeux du déploiement de l’intelligence artificielle en santé. ACTUALITES PHARMACEUTIQUES 2021. [DOI: 10.1016/j.actpha.2021.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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38
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Lo Muzio FP, Rozzi G, Rossi S, Luciani GB, Foresti R, Cabassi A, Fassina L, Miragoli M. Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects. J Clin Med 2021; 10:5330. [PMID: 34830612 PMCID: PMC8623430 DOI: 10.3390/jcm10225330] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 11/08/2021] [Accepted: 11/10/2021] [Indexed: 12/21/2022] Open
Abstract
The human right ventricle is barely monitored during open-chest surgery due to the absence of intraoperative imaging techniques capable of elaborating its complex function. Accordingly, artificial intelligence could not be adopted for this specific task. We recently proposed a video-based approach for the real-time evaluation of the epicardial kinematics to support medical decisions. Here, we employed two supervised machine learning algorithms based on our technique to predict the patients' outcomes before chest closure. Videos of the beating hearts were acquired before and after pulmonary valve replacement in twelve Tetralogy of Fallot patients and recordings were properly labeled as the "unhealthy" and "healthy" classes. We extracted frequency-domain-related features to train different supervised machine learning models and selected their best characteristics via 10-fold cross-validation and optimization processes. Decision surfaces were built to classify two additional patients having good and unfavorable clinical outcomes. The k-nearest neighbors and support vector machine showed the highest prediction accuracy; the patients' class was identified with a true positive rate ≥95% and the decision surfaces correctly classified the additional patients in the "healthy" (good outcome) or "unhealthy" (unfavorable outcome) classes. We demonstrated that classifiers employed with our video-based technique may aid cardiac surgeons in decision making before chest closure.
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Affiliation(s)
- Francesco Paolo Lo Muzio
- Department of Surgery, Dentistry, Pediatrics and Gynecology, University of Verona, 37134 Verona, Italy; (F.P.L.M.); (G.R.); (G.B.L.)
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (S.R.); (R.F.); (A.C.)
| | - Giacomo Rozzi
- Department of Surgery, Dentistry, Pediatrics and Gynecology, University of Verona, 37134 Verona, Italy; (F.P.L.M.); (G.R.); (G.B.L.)
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (S.R.); (R.F.); (A.C.)
- Humanitas Research Hospital—IRCCS, Via Manzoni 56, 20089 Rozzano, MI, Italy
| | - Stefano Rossi
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (S.R.); (R.F.); (A.C.)
| | - Giovanni Battista Luciani
- Department of Surgery, Dentistry, Pediatrics and Gynecology, University of Verona, 37134 Verona, Italy; (F.P.L.M.); (G.R.); (G.B.L.)
| | - Ruben Foresti
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (S.R.); (R.F.); (A.C.)
| | - Aderville Cabassi
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (S.R.); (R.F.); (A.C.)
| | - Lorenzo Fassina
- Department of Electrical, Computer and Biomedical Engineering (DIII), University of Pavia, 27100 Pavia, Italy
| | - Michele Miragoli
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy; (S.R.); (R.F.); (A.C.)
- Humanitas Research Hospital—IRCCS, Via Manzoni 56, 20089 Rozzano, MI, Italy
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Latus H, Meierhofer C. Role of cardiovascular magnetic resonance in pediatric pulmonary hypertension-novel concepts and imaging biomarkers. Cardiovasc Diagn Ther 2021; 11:1057-1069. [PMID: 34527532 DOI: 10.21037/cdt-20-270] [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: 02/29/2020] [Accepted: 04/15/2020] [Indexed: 11/06/2022]
Abstract
Pulmonary hypertension (PH) in children is a heterogenous disease of the small pulmonary arteries characterized by a progressive increase in pulmonary vascular resistance. Despite adequate medical therapy, long-term pressure overload is frequently associated with a progressive course leading to right ventricular failure and ultimately death. Invasive hemodynamic assessment by cardiac catheterization is crucial for initial diagnosis, risk stratification and therapeutic strategy. Although echocardiography remains the most important imaging modality for the assessment of right ventricular function and pulmonary hemodynamics, cardiovascular magnetic resonance (CMR) has emerged as a valuable non-invasive imaging technique that enables comprehensive evaluation of biventricular performance, blood flow, morphology and the myocardial tissue. In this review, we summarize the principles and applications of CMR in the evaluation of pediatric PH patients and present an update about novel CMR based concepts and imaging biomarkers that may provide further diagnostic, therapeutic and prognostic information.
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Affiliation(s)
- Heiner Latus
- Clinic for Congenital Heart Disease and Pediatric Cardiology, German Heart Center Munich, Munich, Germany
| | - Christian Meierhofer
- Clinic for Congenital Heart Disease and Pediatric Cardiology, German Heart Center Munich, Munich, Germany
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Kwan AC, Salto G, Cheng S, Ouyang D. Artificial Intelligence in Computer Vision: Cardiac MRI and Multimodality Imaging Segmentation. CURRENT CARDIOVASCULAR RISK REPORTS 2021; 15:18. [PMID: 35693045 PMCID: PMC9187294 DOI: 10.1007/s12170-021-00678-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/03/2021] [Indexed: 12/17/2022]
Abstract
Purpose of Review Anatomical segmentation has played a major role within clinical cardiology. Novel techniques through artificial intelligence-based computer vision have revolutionized this process through both automation and novel applications. This review discusses the history and clinical context of cardiac segmentation to provide a framework for a survey of recent manuscripts in artificial intelligence and cardiac segmentation. We aim to clarify for the reader the clinical question of "Why do we segment?" in order to understand the question of "Where is current research and where should be?". Recent Findings There has been increasing research in cardiac segmentation in recent years. Segmentation models are most frequently based on a U-Net structure. Multiple innovations have been added in terms of pre-processing or connection to analysis pipelines. Cardiac MRI is the most frequently segmented modality, which is due in part to the presence of publically-available, moderately sized, computer vision competition datasets. Further progress in data availability, model explanation, and clinical integration are being pursued. Summary The task of cardiac anatomical segmentation has experienced massive strides forward within the past five years due to convolutional neural networks. These advances provide a basis for streamlining image analysis, and a foundation for further analysis both by computer and human systems. While technical advances are clear, clinical benefit remains nascent. Novel approaches may improve measurement precision by decreasing inter-reader variability and appear to also have the potential for larger-reaching effects in the future within integrated analysis pipelines.
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Affiliation(s)
- Alan C Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Gerran Salto
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA
- Framingham Heart Study, Framingham, MA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA
- Framingham Heart Study, Framingham, MA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
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Tanaka I, Furukawa T, Morise M. The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence. Cancer Cell Int 2021; 21:454. [PMID: 34446006 PMCID: PMC8393743 DOI: 10.1186/s12935-021-02165-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 08/19/2021] [Indexed: 12/12/2022] Open
Abstract
Comprehensive analysis of omics data, such as genome, transcriptome, proteome, metabolome, and interactome, is a crucial technique for elucidating the complex mechanism of cancer onset and progression. Recently, a variety of new findings have been reported based on multi-omics analysis in combination with various clinical information. However, integrated analysis of multi-omics data is extremely labor intensive, making the development of new analysis technology indispensable. Artificial intelligence (AI), which has been under development in recent years, is quickly becoming an effective approach to reduce the labor involved in analyzing large amounts of complex data and to obtain valuable information that is often overlooked in manual analysis and experiments. The use of AI, such as machine learning approaches and deep learning systems, allows for the efficient analysis of massive omics data combined with accurate clinical information and can lead to comprehensive predictive models that will be desirable for further developing individual treatment strategies of immunotherapy and molecular target therapy. Here, we aim to review the potential of AI in the integrated analysis of omics data and clinical information with a special focus on recent advances in the discovery of new biomarkers and the future direction of personalized medicine in non-small lung cancer.
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Affiliation(s)
- Ichidai Tanaka
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan.
| | - Taiki Furukawa
- Center for Healthcare Information Technology (C-HiT), Nagoya University, Nagoya, Japan
| | - Masahiro Morise
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
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de Perrot M, Gopalan D, Jenkins D, Lang IM, Fadel E, Delcroix M, Benza R, Heresi GA, Kanwar M, Granton JT, McInnis M, Klok FA, Kerr KM, Pepke-Zaba J, Toshner M, Bykova A, Armini AMD, Robbins IM, Madani M, McGiffin D, Wiedenroth CB, Mafeld S, Opitz I, Mercier O, Uber PA, Frantz RP, Auger WR. Evaluation and management of patients with chronic thromboembolic pulmonary hypertension - consensus statement from the ISHLT. J Heart Lung Transplant 2021; 40:1301-1326. [PMID: 34420851 DOI: 10.1016/j.healun.2021.07.020] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 07/22/2021] [Indexed: 02/08/2023] Open
Abstract
ISHLT members have recognized the importance of a consensus statement on the evaluation and management of patients with chronic thromboembolic pulmonary hypertension. The creation of this document required multiple steps, including the engagement of the ISHLT councils, approval by the Standards and Guidelines Committee, identification and selection of experts in the field, and the development of 6 working groups. Each working group provided a separate section based on an extensive literature search. These sections were then coalesced into a single document that was circulated to all members of the working groups. Key points were summarized at the end of each section. Due to the limited number of comparative trials in this field, the document was written as a literature review with expert opinion rather than based on level of evidence.
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Affiliation(s)
- Marc de Perrot
- Division of Thoracic Surgery, Toronto General Hospital, Toronto, Ontario, Canada.
| | - Deepa Gopalan
- Department of Radiology, Imperial College Healthcare NHS Trust, London & Cambridge University Hospital, Cambridge, UK
| | - David Jenkins
- National Pulmonary Endarterectomy Service, Department of Cardiothoracic Surgery, Papworth Hospital, Cambridge, UK
| | - Irene M Lang
- Department of Cardiology, Pulmonary Hypertension Unit, Medical University of Vienna, Vienna, Austria
| | - Elie Fadel
- Department of Thoracic and Vascular Surgery and Heart Lung Transplantation, Marie-Lannelongue Hospital, Paris Saclay University, Le Plessis-Robinson, France
| | - Marion Delcroix
- Clinical Department of Respiratory Diseases, Pulmonary Hypertension Centre, UZ Leuven, Leuven, Belgium; Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism (CHROMETA), KU, Leuven, Belgium
| | - Raymond Benza
- Division of Cardiovascular Medicine, The Ohio State University, Columbus, Ohio
| | - Gustavo A Heresi
- Department of Pulmonary and Critical Care Medicine, Respiratory Institute, Cleveland Clinic, Cleveland, Ohio
| | - Manreet Kanwar
- Cardiovascular Institute, Allegheny Health Network, Pittsburgh, Pennsylvania
| | - John T Granton
- Division of Respirology, University Health Network, Toronto, Ontario, Canada
| | - Micheal McInnis
- Joint Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada
| | - Frederikus A Klok
- Department of Medicine, Thrombosis and Hemostasis, Leiden University Medical Center, Leiden, The Netherlands
| | - Kim M Kerr
- University of California San Diego Medical Health, Division of Pulmonary Critical Care and Sleep Medicine, San Diego, California
| | - Joanna Pepke-Zaba
- Pulmonary Vascular Disease Unit, Royal Papworth Hospital NHS foundation Trust, Cambridge, Cambridgeshire, UK
| | - Mark Toshner
- Pulmonary Vascular Disease Unit, Royal Papworth Hospital NHS foundation Trust, Cambridge, Cambridgeshire, UK; Heart Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Anastasia Bykova
- Division of Thoracic Surgery, Toronto General Hospital, Toronto, Ontario, Canada
| | - Andrea M D' Armini
- Unit of Cardiac Surgery, Intrathoracic-Trasplantation and Pulmonary Hypertension, University of Pavia, Foundation I.R.C.C.S. Policlinico San Matteo, Pavia, Italy
| | - Ivan M Robbins
- Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Michael Madani
- Department of Cardiovascular and Thoracic Surgery, University of California San Diego, La Jolla, California
| | - David McGiffin
- Department of Cardiothoracic Surgery, The Alfred Hospital and Monash University, Melbourne, VIC, Australia
| | - Christoph B Wiedenroth
- Department of Thoracic Surgery, Campus Kerckhoff of the University of Giessen, Kerckhoff Heart and Thorax Center, Bad Nauheim, Germany
| | - Sebastian Mafeld
- Division of Vascular and Interventional Radiology, Joint Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada
| | - Isabelle Opitz
- Department of Thoracic Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Olaf Mercier
- Department of Thoracic and Vascular Surgery and Heart Lung Transplantation, Marie-Lannelongue Hospital, Paris Saclay University, Le Plessis-Robinson, France
| | - Patricia A Uber
- Pauley Heart Center, Virginia Commonwealth University Health System, Richmond, Virginia
| | - Robert P Frantz
- Department of Cardiovascular Disease, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - William R Auger
- Pulmonary Hypertension and CTEPH Research Program, Temple Heart and Vascular Institute, Temple University, Lewis Katz School of Medicine, Philadelphia, Pennsylvania
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Sweatt AJ, Reddy R, Rahaghi FN, Al-Naamani N. What's new in pulmonary hypertension clinical research: lessons from the best abstracts at the 2020 American Thoracic Society International Conference. Pulm Circ 2021; 11:20458940211040713. [PMID: 34471517 PMCID: PMC8404658 DOI: 10.1177/20458940211040713] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 07/26/2021] [Indexed: 12/23/2022] Open
Abstract
In this conference paper, we review the 2020 American Thoracic Society International Conference session titled, "What's New in Pulmonary Hypertension Clinical Research: Lessons from the Best Abstracts". This virtual mini-symposium took place on 21 October 2020, in lieu of the annual in-person ATS International Conference which was cancelled due to the COVID-19 pandemic. Seven clinical research abstracts were selected for presentation in the session, which encompassed five major themes: (1) standardizing diagnosis and management of pulmonary hypertension, (2) improving risk assessment in pulmonary arterial hypertension, (3) evaluating biomarkers of disease activity, (4) understanding metabolic dysregulation across the spectrum of pulmonary hypertension, and (5) advancing knowledge in chronic thromboembolic pulmonary hypertension. Focusing on these five thematic contexts, we review the current state of knowledge, summarize presented research abstracts, appraise their significance and limitations, and then discuss relevant future directions in pulmonary hypertension clinical research.
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Affiliation(s)
- Andrew J. Sweatt
- Division of Pulmonary, Allergy and Critical Care Medicine, Stanford University, Stanford, CA, USA
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford, CA, USA
| | - Raju Reddy
- Division of Pulmonary and Critical Care Medicine, Oregon Health and Science University, Portland, OR, USA
| | - Farbod N. Rahaghi
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Nadine Al-Naamani
- Division of Pulmonary and Critical Care Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - on behalf of the American Thoracic Society Pulmonary Circulation Assembly Early Career Working Group
- Division of Pulmonary, Allergy and Critical Care Medicine, Stanford University, Stanford, CA, USA
- Vera Moulton Wall Center for Pulmonary Vascular Disease, Stanford, CA, USA
- Division of Pulmonary and Critical Care Medicine, Oregon Health and Science University, Portland, OR, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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Bordones-Crom A, Patnaik SS, Menon PG, Murali S, Finol E. Morphological Analysis of the Right Ventricular Endocardial Wall in Pulmonary Hypertension. J Biomech Eng 2021; 143:074504. [PMID: 33704381 DOI: 10.1115/1.4050457] [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: 06/24/2020] [Indexed: 11/08/2022]
Abstract
Pulmonary hypertension (PH) is a chronic progressive disease diagnosed when the pressure in the main pulmonary artery, assessed by right heart catheterization (RHC), is greater than 25 mmHg. Changes in the pulmonary vasculature due to the high pressure yield an increase in the right ventricle (RV) afterload. This starts a remodeling process during which the ventricle exhibits changes in shape and eventually fails. RV models were obtained from the segmentation of cardiac magnetic resonance images at baseline and 1-year follow-up for a pilot study that involved 12 PH and 7 control subjects. The models were used to create surface meshes of the geometry and to compute the principal, mean, and Gaussian curvatures. Ten global curvature indices were calculated for each of the RV endocardial wall reconstructions at the end-diastolic volume (EDV) and end-systolic volume (ESV) phases of the cardiac cycle. Statistical analysis of the data was performed to discern if there are significant differences in the curvature indices between controls and the PH group, as well as between the baseline and follow-up phases for the PH subjects. Six curvature indices, namely, the Gaussian curvature at ESV, the mean curvature at EDV and ESV, the L2-norm of the mean curvature at ESV, and the L2-norm of the major principal curvature at EDV and ESV, were found to be significantly different between controls and PH subjects (p < 0.05). We infer that these geometry measures could be used as indicators of RV endocardial wall morphology changes. Two global parameters, the Gaussian and mean curvatures at ESV, showed significant changes at the one-year follow-up for the PH subjects (p < 0.05). The aforementioned geometry measures to assess changes in RV shape could be used as part of a noninvasive computational tool to aid clinicians in PH diagnostic and progression assessment, and to evaluate the effectiveness of treatment.
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Affiliation(s)
- Alifer Bordones-Crom
- UTSA/UTHSA Joint Graduate Program in Biomedical Engineering, University of Texas at San Antonio, San Antonio, TX 78249
| | - Sourav S Patnaik
- Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX 78249; Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080
| | - Prahlad G Menon
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260
| | - Srinivas Murali
- Allegheny Health Network, Allegheny General Hospital, Pittsburgh, PA 15212
| | - Ender Finol
- UTSA/UTHSA Joint Graduate Program in Biomedical Engineering, University of Texas at San Antonio, San Antonio, TX 78249; Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX 78249
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Haq IU, Haq I, Xu B. Artificial intelligence in personalized cardiovascular medicine and cardiovascular imaging. Cardiovasc Diagn Ther 2021; 11:911-923. [PMID: 34295713 DOI: 10.21037/cdt.2020.03.09] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 03/05/2020] [Indexed: 12/27/2022]
Abstract
The collection of large, heterogeneous electronic datasets and imaging from patients with cardiovascular disease (CVD) has lent itself to the use of sophisticated analysis using artificial intelligence (AI). AI techniques such as machine learning (ML) are able to identify relationships between data points by linking input to output variables using a combination of different functions, such as neural networks. In cardiovascular medicine, this is especially pertinent for classification, diagnosis, risk prediction and treatment guidance. Common cardiovascular data sources from patients include genomic data, cardiovascular imaging, wearable sensors and electronic health records (EHR). Leveraging AI in analysing such data points: (I) for clinicians: more accurate and streamlined image interpretation and diagnosis; (II) for health systems: improved workflow and reductions in medical errors; (III) for patients: promoting health with further education and promoting primary and secondary cardiovascular health prevention. This review addresses the need for AI in cardiovascular medicine by reviewing recent literature in different cardiovascular imaging modalities: electrocardiography, echocardiography, cardiac computed tomography, cardiac nuclear imaging, and cardiac magnetic resonance (CMR) imaging as well as the role of EHR. This review aims to conceptulise these studies in relation to their clinical applications, potential limitations and future opportunities and directions.
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Affiliation(s)
- Ikram-Ul Haq
- Imperial College London Faculty of Medicine, London, UK
| | - Iqraa Haq
- Imperial College London Faculty of Medicine, London, UK
| | - Bo Xu
- Section of Cardiovascular Imaging, Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Family Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
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46
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Prediction of the Hypertension Risk of the Elderly in Built Environments Based on the LSTM Deep Learning and Bayesian Fitting Method. SUSTAINABILITY 2021. [DOI: 10.3390/su13105724] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Hypertension has become the greatest risk factor for death in elderly populations. As factors influencing cardiovascular disease, indoor environmental parameters pose potential risks for older adults. In this study, elderly residents in Dalian (Liaoning Province, China) urban dwellings were selected as the research subjects, and the environmental parameters of the dwellings’ main activity rooms and the blood pressure parameters of the older adults were measured. Based on the Long Short-Term Memory (LSTM) deep learning algorithm and Bayesian fitting method, a hypertension disease model was established using the long-term environmental parameters to predict the hypertension risk of older adults in their building’s environment. The results showed that temperature, humidity, and some air quality parameters had an impact on blood pressure under single environmental factor, and the comprehensive environmental risks of high systolic blood pressure, high diastolic blood pressure, and high blood pressure were 16.44%, 0%, and 16.44% for the male elderly and 14.11%, 7.14%, and 17.55% for the female elderly, respectively. By comparing the results for the blood pressure measurement and prediction, it can be observed that the risk error of hypertension obtained by the algorithm maintains the variables’ relationship, and the result of the algorithm is reliable in this period. This technology can provide a basis for measuring environmental parameters and will be conducive to the development of an ecological smart building environment.
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47
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Ankenbrand MJ, Lohr D, Schlötelburg W, Reiter T, Wech T, Schreiber LM. Deep learning-based cardiac cine segmentation: Transfer learning application to 7T ultrahigh-field MRI. Magn Reson Med 2021; 86:2179-2191. [PMID: 34002412 DOI: 10.1002/mrm.28822] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 03/18/2021] [Accepted: 04/09/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE Artificial neural networks show promising performance in automatic segmentation of cardiac MRI. However, training requires large amounts of annotated data and generalization to different vendors, field strengths, sequence parameters, and pathologies is limited. Transfer learning addresses this challenge, but specific recommendations regarding type and amount of data required is lacking. In this study, we assess data requirements for transfer learning to experimental cardiac MRI at 7T where the segmentation task can be challenging. In addition, we provide guidelines, tools, and annotated data to enable transfer learning approaches by other researchers and clinicians. METHODS A publicly available segmentation model was used to annotate a publicly available data set. This labeled data set was subsequently used to train a neural network for segmentation of left ventricle and myocardium in cardiac cine MRI. The network is used as starting point for transfer learning to 7T cine data of healthy volunteers (n = 22; 7873 images) by updating the pre-trained weights. Structured and random data subsets of different sizes were used to systematically assess data requirements for successful transfer learning. RESULTS Inconsistencies in the publically available data set were corrected, labels created, and a neural network trained. On 7T cardiac cine images the model pre-trained on public imaging data, acquired at 1.5T and 3T, achieved DICELV = 0.835 and DICEMY = 0.670. Transfer learning using 7T cine data and ImageNet weight initialization improved model performance to DICELV = 0.900 and DICEMY = 0.791. Using only end-systolic and end-diastolic images reduced training data by 90%, with no negative impact on segmentation performance (DICELV = 0.908, DICEMY = 0.805). CONCLUSIONS This work demonstrates and quantifies the benefits of transfer learning for cardiac cine image segmentation. We provide practical guidelines for researchers planning transfer learning projects in cardiac MRI and make data, models, and code publicly available.
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Affiliation(s)
- Markus Johannes Ankenbrand
- Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
| | - David Lohr
- Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
| | - Wiebke Schlötelburg
- Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany.,Department of Radiology, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Theresa Reiter
- Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany.,Department of Internal Medicine I, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Tobias Wech
- Department of Radiology, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Laura Maria Schreiber
- Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
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Rutkowski DR, Roldán-Alzate A, Johnson KM. Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data. Sci Rep 2021; 11:10240. [PMID: 33986368 PMCID: PMC8119419 DOI: 10.1038/s41598-021-89636-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 04/29/2021] [Indexed: 12/12/2022] Open
Abstract
Blood flow metrics obtained with four-dimensional (4D) flow phase contrast (PC) magnetic resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular analysis. However, limitations in both quantitative and qualitative analyses can result from errors inherent to PC MRI. One method that excels in creating low-error, physics-based, velocity fields is computational fluid dynamics (CFD). Augmentation of cerebral 4D flow MRI data with CFD-informed neural networks may provide a method to produce highly accurate physiological flow fields. In this preliminary study, the potential utility of such a method was demonstrated by using high resolution patient-specific CFD data to train a convolutional neural network, and then using the trained network to enhance MRI-derived velocity fields in cerebral blood vessel data sets. Through testing on simulated images, phantom data, and cerebrovascular 4D flow data from 20 patients, the trained network successfully de-noised flow images, decreased velocity error, and enhanced near-vessel-wall velocity quantification and visualization. Such image enhancement can improve experimental and clinical qualitative and quantitative cerebrovascular PC MRI analysis.
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Affiliation(s)
- David R Rutkowski
- Mechanical Engineering, University of Wisconsin, Madison, WI, USA
- Radiology, University of Wisconsin, 1111 Highland Ave, Madison, WI, USA
| | - Alejandro Roldán-Alzate
- Mechanical Engineering, University of Wisconsin, Madison, WI, USA
- Radiology, University of Wisconsin, 1111 Highland Ave, Madison, WI, USA
| | - Kevin M Johnson
- Radiology, University of Wisconsin, 1111 Highland Ave, Madison, WI, USA.
- Medical Physics, University of Wisconsin, 1111 Highland Ave, Madison, WI, USA.
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Nabi W, Bansal A, Xu B. Applications of artificial intelligence and machine learning approaches in echocardiography. Echocardiography 2021; 38:982-992. [PMID: 33982820 DOI: 10.1111/echo.15048] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 03/18/2021] [Accepted: 03/29/2021] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence and machine learning approaches have become increasingly applied in the field of echocardiography to streamline diagnostic and prognostic assessments, and to support treatment decisions. Artificial intelligence and machine learning have been applied to aid image acquisition and automation. They have also been applied to the integration of clinical and imaging data. Applications of artificial intelligence and machine learning approaches in echocardiography in conjunction with health information databases may be promising in improving the classification and treatment of many cardiac conditions. This review article provides an overview of the applications of artificial intelligence and machine learning approaches in echocardiography.
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Affiliation(s)
- Wafa Nabi
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Agam Bansal
- Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Bo Xu
- Section of Cardiovascular Imaging, Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Miller Family Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
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50
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Alzu’bi A, Najadat H, Doulat W, Al-Shari O, Zhou L. Predicting the recurrence of breast cancer using machine learning algorithms. MULTIMEDIA TOOLS AND APPLICATIONS 2021; 80:13787-13800. [DOI: 10.1007/s11042-020-10448-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Revised: 08/24/2020] [Accepted: 12/22/2020] [Indexed: 08/29/2023]
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