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Lüscher TF, Wenzl FA, D'Ascenzo F, Friedman PA, Antoniades C. Artificial intelligence in cardiovascular medicine: clinical applications. Eur Heart J 2024:ehae465. [PMID: 39158472 DOI: 10.1093/eurheartj/ehae465] [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: 03/18/2024] [Revised: 06/07/2024] [Accepted: 07/03/2024] [Indexed: 08/20/2024] Open
Abstract
Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk-benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.
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Affiliation(s)
- Thomas F Lüscher
- Royal Brompton and Harefield Hospitals, London, UK
- National Heart and Lung Institute, Imperial College London, UK
- Cardiovascular Academic Group, King's College, London, UK
- Center for Molecular Cardiology, University of Zurich, Wagistrasse 12, 8952 Schlieren - Zurich, Switzerland
| | - Florian A Wenzl
- Center for Molecular Cardiology, University of Zurich, Wagistrasse 12, 8952 Schlieren - Zurich, Switzerland
- National Disease Registration and Analysis Service, NHS, London, UK
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- Department of Clinical Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Fabrizio D'Ascenzo
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza Hospital, Turin, Italy
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic and Mayo Foundation, Rochester, MN, USA
| | - Charalambos Antoniades
- Acute Multidisciplinary Imaging and Interventional Centre, RDM Division of Cardiovascular Medicine, University of Oxford, Headley Way, Headington, Oxford OX39DU, UK
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Xu H, Wu Y. G2ViT: Graph Neural Network-Guided Vision Transformer Enhanced Network for retinal vessel and coronary angiograph segmentation. Neural Netw 2024; 176:106356. [PMID: 38723311 DOI: 10.1016/j.neunet.2024.106356] [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: 10/11/2023] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 06/17/2024]
Abstract
Blood vessel segmentation is a crucial stage in extracting morphological characteristics of vessels for the clinical diagnosis of fundus and coronary artery disease. However, traditional convolutional neural networks (CNNs) are confined to learning local vessel features, making it challenging to capture the graph structural information and fail to perceive the global context of vessels. Therefore, we propose a novel graph neural network-guided vision transformer enhanced network (G2ViT) for vessel segmentation. G2ViT skillfully orchestrates the Convolutional Neural Network, Graph Neural Network, and Vision Transformer to enhance comprehension of the entire graphical structure of blood vessels. To achieve deeper insights into the global graph structure and higher-level global context cognizance, we investigate a graph neural network-guided vision transformer module. This module constructs graph-structured representation in an unprecedented manner using the high-level features extracted by CNNs for graph reasoning. To increase the receptive field while ensuring minimal loss of edge information, G2ViT introduces a multi-scale edge feature attention module (MEFA), leveraging dilated convolutions with different dilation rates and the Sobel edge detection algorithm to obtain multi-scale edge information of vessels. To avoid critical information loss during upsampling and downsampling, we design a multi-level feature fusion module (MLF2) to fuse complementary information between coarse and fine features. Experiments on retinal vessel datasets (DRIVE, STARE, CHASE_DB1, and HRF) and coronary angiography datasets (DCA1 and CHUAC) indicate that the G2ViT excels in robustness, generality, and applicability. Furthermore, it has acceptable inference time and computational complexity and presents a new solution for blood vessel segmentation.
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Affiliation(s)
- Hao Xu
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China; College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Yun Wu
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China; College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.
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Thribhuvan Reddy D, Grewal I, García Pinzon LF, Latchireddy B, Goraya S, Ali Alansari B, Gadwal A. The Role of Artificial Intelligence in Healthcare: Enhancing Coronary Computed Tomography Angiography for Coronary Artery Disease Management. Cureus 2024; 16:e61523. [PMID: 38957241 PMCID: PMC11218716 DOI: 10.7759/cureus.61523] [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] [Accepted: 06/02/2024] [Indexed: 07/04/2024] Open
Abstract
This review aims to explore the potential of artificial intelligence (AI) in coronary CT angiography (CCTA), a key tool for diagnosing coronary artery disease (CAD). Because CAD is still a major cause of death worldwide, effective and accurate diagnostic methods are required to identify and manage the condition. CCTA certainly is a noninvasive alternative for diagnosing CAD, but it requires a large amount of data as input. We intend to discuss the idea of incorporating AI into CCTA, which enhances its diagnostic accuracy and operational efficiency. Using such AI technologies as machine learning (ML) and deep learning (DL) tools, CCTA images are automated to perfection and the analysis is significantly refined. It enables the characterization of a plaque, assesses the severity of the stenosis, and makes more accurate risk stratifications than traditional methods, with pinpoint accuracy. Automating routine tasks through AI-driven CCTA will reduce the radiologists' workload considerably, which is a standard benefit of such technologies. More importantly, it would enable radiologists to allocate more time and expertise to complex cases, thereby improving overall patient care. However, the field of AI in CCTA is not without its challenges, which include data protection, algorithm transparency, as well as criteria for standardization encoding. Despite such obstacles, it appears that the integration of AI technology into CCTA in the future holds great promise for keeping CAD itself in check, thereby aiding the fight against this disease and begetting better clinical outcomes and more optimized modes of healthcare. Future research on AI algorithms for CCTA, making ethical use of AI, and thereby overcoming the technical and clinical barriers to widespread adoption of this new tool, will hopefully pave the way for profound AI-driven transformations in healthcare.
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Affiliation(s)
| | - Inayat Grewal
- Department of Medicine, Government Medical College and Hospital, Chandigarh, IND
| | | | | | - Simran Goraya
- Department of Medicine, Kharkiv National Medical University, Kharkiv, UKR
| | | | - Aishwarya Gadwal
- Department of Radiodiagnosis, St. John's Medical College and Hospital, Bengaluru, IND
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Sawant R, Acharya S, Kumar S, Chaudhari P. Quantitative Angiography: The Dawn of a New Era in Cardiovascular Medicine. Cureus 2024; 16:e61407. [PMID: 38953063 PMCID: PMC11215030 DOI: 10.7759/cureus.61407] [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: 05/20/2024] [Accepted: 05/31/2024] [Indexed: 07/03/2024] Open
Abstract
This comprehensive review explores the transformative role of quantitative angiography in the landscape of cardiovascular medicine. Tracing the historical evolution of cardiovascular diagnostics, we emphasize the significance of angiography in diagnosis and treatment. The primary focus on quantitative angiography reveals its capacity to move beyond qualitative assessments, providing clinicians with precise measurements and objective parameters. This paradigm shift enhances diagnostic accuracy, promising far-reaching implications for the future of cardiovascular medicine. The ability to tailor interventions based on meticulous measurements optimizes therapeutic strategies and positions the field on the brink of a new era where personalized approaches become the norm. However, challenges such as image quality, radiation exposure, and interpretation variability persist, necessitating a collective call to action for continued research and development. As we confront these issues, collaborative efforts across disciplines are essential to refine existing technologies and usher in innovative solutions. This review concludes with a resounding call for ongoing research initiatives, large-scale clinical studies, and collective commitment to propel quantitative angiography into a universally accepted standard, ensuring its full realization in enhancing patient care and outcomes in cardiovascular medicine.
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Affiliation(s)
- Rucha Sawant
- Internal Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Sourya Acharya
- Internal Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Sunil Kumar
- Internal Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Pranav Chaudhari
- Internal Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Chang SS, Lin CT, Wang WC, Hsu KC, Wu YL, Liu CH, Fann YC. Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images. Sci Rep 2024; 14:6640. [PMID: 38503839 PMCID: PMC10951254 DOI: 10.1038/s41598-024-57198-5] [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: 10/17/2023] [Accepted: 03/15/2024] [Indexed: 03/21/2024] Open
Abstract
Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vessels using 100 labeled angiographies from angiographic images. SE-RegUNet incorporates RegNet encoders and squeeze-and-excitation blocks to enhance feature extraction. A dual-phase image preprocessing strategy further improves the model's performance, employing unsharp masking and contrast-limited adaptive histogram equalization. Following fivefold cross-validation and Ranger21 optimization, the SE-RegUNet 4GF model emerged as the most effective, evidenced by performance metrics such as a Dice score of 0.72 and an accuracy of 0.97. Its potential for real-world application is highlighted by its ability to process images at 41.6 frames per second. External validation on the DCA1 dataset demonstrated the model's consistent robustness, achieving a Dice score of 0.76 and an accuracy of 0.97. The SE-RegUNet 4GF model's precision in segmenting blood vessels in coronary angiographies showcases its remarkable efficiency and accuracy. However, further development and clinical testing are necessary before it can be routinely implemented in medical practice.
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Affiliation(s)
- Shih-Sheng Chang
- Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan
| | - Ching-Ting Lin
- Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan
| | - Wei-Chun Wang
- Department of Neurology, China Medical University Hospital, Taichung, Taiwan
- Neuroscience and Brain Disease Center, China Medical University, Taichung, Taiwan
- Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan
| | - Kai-Cheng Hsu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan
- Department of Neurology, China Medical University Hospital, Taichung, Taiwan
- Neuroscience and Brain Disease Center, China Medical University, Taichung, Taiwan
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan
| | - Ya-Lun Wu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan
| | - Chia-Hao Liu
- Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Yang C Fann
- Division of Intramural Research, National Institute of Neurological Disorders and Stroke, National Institutes of Health, 35 Convent Dr., Bethesda, MD, 20892, USA.
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Molenaar MA, Bouma BJ, Asselbergs FW, Verouden NJ, Selder JL, Chamuleau SAJ, Schuuring MJ. Explainable machine learning using echocardiography to improve risk prediction in patients with chronic coronary syndrome. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:170-182. [PMID: 38505485 PMCID: PMC10944683 DOI: 10.1093/ehjdh/ztae001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 12/21/2023] [Accepted: 12/29/2023] [Indexed: 03/21/2024]
Abstract
Aims The European Society of Cardiology guidelines recommend risk stratification with limited clinical parameters such as left ventricular (LV) function in patients with chronic coronary syndrome (CCS). Machine learning (ML) methods enable an analysis of complex datasets including transthoracic echocardiography (TTE) studies. We aimed to evaluate the accuracy of ML using clinical and TTE data to predict all-cause 5-year mortality in patients with CCS and to compare its performance with traditional risk stratification scores. Methods and results Data of consecutive patients with CCS were retrospectively collected if they attended the outpatient clinic of Amsterdam UMC location AMC between 2015 and 2017 and had a TTE assessment of the LV function. An eXtreme Gradient Boosting (XGBoost) model was trained to predict all-cause 5-year mortality. The performance of this ML model was evaluated using data from the Amsterdam UMC location VUmc and compared with the reference standard of traditional risk scores. A total of 1253 patients (775 training set and 478 testing set) were included, of which 176 patients (105 training set and 71 testing set) died during the 5-year follow-up period. The ML model demonstrated a superior performance [area under the receiver operating characteristic curve (AUC) 0.79] compared with traditional risk stratification tools (AUC 0.62-0.76) and showed good external performance. The most important TTE risk predictors included in the ML model were LV dysfunction and significant tricuspid regurgitation. Conclusion This study demonstrates that an explainable ML model using TTE and clinical data can accurately identify high-risk CCS patients, with a prognostic value superior to traditional risk scores.
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Affiliation(s)
- Mitchel A Molenaar
- Department of Cardiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Berto J Bouma
- Department of Cardiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Niels J Verouden
- Department of Cardiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Jasper L Selder
- Department of Cardiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Steven A J Chamuleau
- Department of Cardiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Mark J Schuuring
- Department of Cardiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
- Department of Cardiology, Circulatory Health Laboratory, University Medical Center Utrecht, Utrecht, The Netherlands
- Circulatory Health UMC Utrecht, Utrecht, The Netherlands
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Kresoja KP, Unterhuber M, Wachter R, Thiele H, Lurz P. A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction. Basic Res Cardiol 2023; 118:10. [PMID: 36939941 PMCID: PMC10027799 DOI: 10.1007/s00395-023-00982-7] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/21/2023] [Accepted: 02/26/2023] [Indexed: 03/21/2023]
Abstract
A modern-day physician is faced with a vast abundance of clinical and scientific data, by far surpassing the capabilities of the human mind. Until the last decade, advances in data availability have not been accompanied by analytical approaches. The advent of machine learning (ML) algorithms might improve the interpretation of complex data and should help to translate the near endless amount of data into clinical decision-making. ML has become part of our everyday practice and might even further change modern-day medicine. It is important to acknowledge the role of ML in prognosis prediction of cardiovascular disease. The present review aims on preparing the modern physician and researcher for the challenges that ML might bring, explaining basic concepts but also caveats that might arise when using these methods. Further, a brief overview of current established classical and emerging concepts of ML disease prediction in the fields of omics, imaging and basic science is presented.
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Affiliation(s)
- Karl-Patrik Kresoja
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany
| | - Matthias Unterhuber
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany
| | - Rolf Wachter
- Department of Cardiology, University Hospital Leipzig, Leipzig, Germany
- Clinic for Cardiology and Pneumology, University Medicine Göttingen, Göttingen, Germany
- German Cardiovascular Research Center (DZHK), Partner Site Göttingen, Göttingen, Germany
| | - Holger Thiele
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany.
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany.
| | - Philipp Lurz
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany.
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany.
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Schuuring MJ, Mischie AN, Caiani EG. Editorial: Digital Solutions in Cardiology. Front Cardiovasc Med 2022; 9:873991. [PMID: 35463763 PMCID: PMC9024208 DOI: 10.3389/fcvm.2022.873991] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 02/23/2022] [Indexed: 11/16/2022] Open
Affiliation(s)
- Mark J. Schuuring
- Department of Cardiology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Alexandru N. Mischie
- Centre Hospitalier Montlucon, Department of Cardiology, Montluçon, France
- International Society of Telemedicine and eHealth, Montluçon, France
| | - Enrico G. Caiani
- Politecnico di Milano, Department of Electronics, Information and Biomedical Engineering, Milan, Italy
- National Council of Research, Institute of Electronics, Information and Telecommunication Engineering, Milan, Italy
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