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Nobre Menezes M, Oliveira CS, Silva JL, Silva BV, Silva Marques J, Guerreiro C, Guedes JP, Oliveira-Santos M, Oliveira AL, Pinto FJ. Old Habits Die Hard: Can AI Help Bring Coronary Angiography Into the 21st Century? JACC. ADVANCES 2024; 3:101093. [PMID: 39055273 PMCID: PMC11269275 DOI: 10.1016/j.jacadv.2024.101093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Affiliation(s)
- Miguel Nobre Menezes
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
- Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa
| | - Catarina Simões Oliveira
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
- Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa
| | | | - Beatriz Valente Silva
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
- Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa
| | - João Silva Marques
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
- Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa
| | - Cláudio Guerreiro
- Cardiology Department, Centro Hospitalar de Vila Nova de Gaia, Vila Nova de Gaia, Portugal
| | - João Pedro Guedes
- Unidade de Hemodinâmica e Cardiologia de Intervenção, Serviço de Cardiologia, Centro Hospitalar Universitário do Algarve, Hospital de Faro, Faro, Portugal
| | - Manuel Oliveira-Santos
- Unidade de Intervenção Cardiovascular, Serviço de Cardiologia do Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Pólo das Ciências da Saúde, Unidade Central, Coimbra, Portugal
| | | | - Fausto J. Pinto
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
- Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa
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Doolub G, Khurshid S, Theriault-Lauzier P, Nolin Lapalme A, Tastet O, So D, Labrecque Langlais E, Cobin D, Avram R. Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges. Can J Cardiol 2024:S0828-282X(24)00443-4. [PMID: 38901544 DOI: 10.1016/j.cjca.2024.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/29/2024] [Accepted: 06/12/2024] [Indexed: 06/22/2024] Open
Abstract
This article reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidly and accurately process data for the prediction and diagnosis of acute cardiac conditions. The review examines AI's impact on patient health across various diagnostic tools such as echocardiography, electrocardiography, coronary angiography, cardiac computed tomography, and magnetic resonance imaging, discusses the regulatory landscape for AI in health care, and categorises AI algorithms by their risk levels. Furthermore, it addresses the challenges of data quality, generalisability, bias, transparency, and regulatory considerations, underscoring the necessity for inclusive data and robust validation processes. The review concludes with future perspectives on integrating AI into clinical workflows and the ongoing need for research, regulation, and innovation to harness AI's full potential in improving acute cardiac care.
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Affiliation(s)
- Gemina Doolub
- Department of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Québec, Canada
| | - Shaan Khurshid
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | - Alexis Nolin Lapalme
- Department of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Québec, Canada; Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada; Mila-Québec AI Institute, Montréal, Québec, Canada
| | - Olivier Tastet
- Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada
| | - Derek So
- University of Ottawa, Heart Institute, Ottawa, Ontario, Canada
| | | | - Denis Cobin
- Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada
| | - Robert Avram
- Department of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Québec, Canada; Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada.
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3
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Elias P, Jain SS, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein AJ, Avram R, Tison GH, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva JN, Maddox TM. Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week. J Am Coll Cardiol 2024; 83:2472-2486. [PMID: 38593946 DOI: 10.1016/j.jacc.2024.03.400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024]
Abstract
Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.
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Affiliation(s)
- Pierre Elias
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA
| | - Sneha S Jain
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Timothy Poterucha
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Michael Randazzo
- Division of Cardiology, University of Chicago Medical Center, Chicago, Illinois, USA
| | | | - Rohan Khera
- Division of Cardiology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Marco Perez
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - David Ouyang
- Division of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - James Pirruccello
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Michael Salerno
- Division of Cardiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Andrew J Einstein
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Robert Avram
- Division of Cardiology, Montreal Heart Institute, Montreal, Quebec, Canada
| | - Geoffrey H Tison
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Girish Nadkarni
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Emma Pierson
- Department of Computer Science, Cornell Tech, New York, New York, USA
| | - Ashley Beecy
- NewYork-Presbyterian Health System, New York, New York, USA; Division of Cardiology, Weill Cornell Medical College, New York, New York, USA
| | - Deepa Kumaraiah
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Chris Haggerty
- Department of Biomedical Informatics Columbia University Irving Medical Center, New York, New York, USA; NewYork-Presbyterian Health System, New York, New York, USA
| | - Jennifer N Avari Silva
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA
| | - Thomas M Maddox
- Division of Cardiology, Washington University School of Medicine, St Louis, Missouri, USA.
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In Kim Y, Roh JH, Kweon J, Kwon H, Chae J, Park K, Lee JH, Jeong JO, Kang DY, Lee PH, Ahn JM, Kang SJ, Park DW, Lee SW, Lee CW, Park SW, Park SJ, Kim YH. Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning. Int J Cardiol 2024; 405:131945. [PMID: 38479496 DOI: 10.1016/j.ijcard.2024.131945] [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] [Received: 11/29/2023] [Revised: 01/24/2024] [Accepted: 03/05/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND Quantitative coronary angiography (QCA) offers objective and reproducible measures of coronary lesions. However, significant inter- and intra-observer variability and time-consuming processes hinder the practical application of on-site QCA in the current clinical setting. This study proposes a novel method for artificial intelligence-based QCA (AI-QCA) analysis of the major vessels and evaluates its performance. METHODS AI-QCA was developed using three deep-learning models trained on 7658 angiographic images from 3129 patients for the precise delineation of lumen boundaries. An automated quantification method, employing refined matching for accurate diameter calculation and iterative updates of diameter trend lines, was embedded in the AI-QCA. A separate dataset of 676 coronary angiography images from 370 patients was retrospectively analyzed to compare AI-QCA with manual QCA performed by expert analysts. A match was considered between manual and AI-QCA lesions when the minimum lumen diameter (MLD) location identified manually coincided with the location identified by AI-QCA. Matched lesions were evaluated in terms of diameter stenosis (DS), MLD, reference lumen diameter (RLD), and lesion length (LL). RESULTS AI-QCA exhibited a sensitivity of 89% in lesion detection and strong correlations with manual QCA for DS, MLD, RLD, and LL. Among 995 matched lesions, most cases (892 cases, 80%) exhibited DS differences ≤10%. Multiple lesions of the major vessels were accurately identified and quantitatively analyzed without manual corrections. CONCLUSION AI-QCA demonstrates promise as an automated tool for analysis in coronary angiography, offering potential advantages for the quantitative assessment of coronary lesions and clinical decision-making.
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Affiliation(s)
- Young In Kim
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jae-Hyung Roh
- Department of Cardiology, Chungnam National University Sejong Hospital, Chungnam National University School of Medicine, Sejong, Republic of Korea
| | - Jihoon Kweon
- Department of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Hwi Kwon
- Department of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jihye Chae
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Keunwoo Park
- Department of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jae-Hwan Lee
- Department of Cardiology, Chungnam National University Sejong Hospital, Chungnam National University School of Medicine, Sejong, Republic of Korea
| | - Jin-Ok Jeong
- Division of Cardiology, Department of Internal Medicine, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon, Republic of Korea
| | - Do-Yoon Kang
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Pil Hyung Lee
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jung-Min Ahn
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Soo-Jin Kang
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Duk-Woo Park
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seung-Whan Lee
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Cheol Whan Lee
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seong-Wook Park
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seung-Jung Park
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young-Hak Kim
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Labrecque Langlais É, Corbin D, Tastet O, Hayek A, Doolub G, Mrad S, Tardif JC, Tanguay JF, Marquis-Gravel G, Tison GH, Kadoury S, Le W, Gallo R, Lesage F, Avram R. Evaluation of stenoses using AI video models applied to coronary angiography. NPJ Digit Med 2024; 7:138. [PMID: 38783037 PMCID: PMC11116436 DOI: 10.1038/s41746-024-01134-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 05/10/2024] [Indexed: 05/25/2024] Open
Abstract
The coronary angiogram is the gold standard for evaluating the severity of coronary artery disease stenoses. Presently, the assessment is conducted visually by cardiologists, a method that lacks standardization. This study introduces DeepCoro, a ground-breaking AI-driven pipeline that integrates advanced vessel tracking and a video-based Swin3D model that was trained and validated on a dataset comprised of 182,418 coronary angiography videos spanning 5 years. DeepCoro achieved a notable precision of 71.89% in identifying coronary artery segments and demonstrated a mean absolute error of 20.15% (95% CI: 19.88-20.40) and a classification AUROC of 0.8294 (95% CI: 0.8215-0.8373) in stenosis percentage prediction compared to traditional cardiologist assessments. When compared to two expert interventional cardiologists, DeepCoro achieved lower variability than the clinical reports (19.09%; 95% CI: 18.55-19.58 vs 21.00%; 95% CI: 20.20-21.76, respectively). In addition, DeepCoro can be fine-tuned to a different modality type. When fine-tuned on quantitative coronary angiography assessments, DeepCoro attained an even lower mean absolute error of 7.75% (95% CI: 7.37-8.07), underscoring the reduced variability inherent to this method. This study establishes DeepCoro as an innovative video-based, adaptable tool in coronary artery disease analysis, significantly enhancing the precision and reliability of stenosis assessment.
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Affiliation(s)
- Élodie Labrecque Langlais
- Department of Electrical Engineering, Polytechnique Montréal, Montreal, QC, Canada
- Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, QC, Canada
| | - Denis Corbin
- Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, QC, Canada
- Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, QC, Canada
| | - Olivier Tastet
- Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, QC, Canada
- Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, QC, Canada
| | - Ahmad Hayek
- Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, QC, Canada
| | - Gemina Doolub
- Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, QC, Canada
| | - Sebastián Mrad
- Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, QC, Canada
| | - Jean-Claude Tardif
- Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, QC, Canada
| | - Jean-François Tanguay
- Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, QC, Canada
| | | | - Geoffrey H Tison
- Department of Medicine, University of California, San Francisco, CA, USA
| | - Samuel Kadoury
- Department of Computer Engineering, Polytechnique Montréal, Montreal, QC, Canada
| | - William Le
- Department of Computer Engineering, Polytechnique Montréal, Montreal, QC, Canada
| | - Richard Gallo
- Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, QC, Canada
| | - Frederic Lesage
- Department of Electrical Engineering, Polytechnique Montréal, Montreal, QC, Canada
- Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, QC, Canada
| | - Robert Avram
- Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, QC, Canada.
- Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, QC, Canada.
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6
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Kageyama S, Serruys PW, Kotoku N, Garg S, Ninomiya K, Masuda S, Morel MA, Taylor CA, Rogers C, Thomsen B, Pontone G, Pompilio G, Puskas JD, Doenst T, La Meir M, Teichgräber U, Gupta H, De Mey J, Andreini D, Onuma Y. Coronary computed tomography angiography-based SYNTAX score for comprehensive assessment of advanced coronary artery disease. J Cardiovasc Comput Tomogr 2024; 18:120-136. [PMID: 37923578 DOI: 10.1016/j.jcct.2023.10.012] [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] [Received: 09/24/2023] [Revised: 10/08/2023] [Accepted: 10/17/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Since the initial attempt to adapt the anatomical SYNTAX score (aSS) to coronary computed tomography angiography (CCTA), CCTA imaging technology has evolved, and is currently used as a "decision-maker" for revascularization strategy in complex coronary artery disease (CAD) and has rendered necessary some updating of the aSS to the CCTA modality. OBJECTIVES The aim is to provide updated definitions of the aSS derived from CCTA in patients with complex CAD undergoing CABG. METHODS The modifications of CCTA-aSS are the following; (i) updated definition and detection criteria of total occlusion (TO) in CCTA based on length assessment, (ii) inclusion of scoring points for serial bifurcations located in one single coronary segment. (iii) inclusion of weighing score points for lesions located distal to a TO, not visualized on conventional coronary angiography, but visible in CCTA, (iv) removal of thrombus and bridging collateral items from the weighing score, considering the limited diagnostic capability of CCTA in detecting these specific lesion characteristics. RESULTS the updated CCTA-aSS was tested in a first-in-man study using the sole guidance of CCTA for the planning and performance of bypass surgery in complex CAD (n = 114). An interobserver analysis showed excellent reproducibility (ICC = 0.96, 95 % confidence interval 0.94-0.97). CONCLUSION The updated CCTA-aSS was implemented in a cohort of patients with complex CAD undergoing CABG with the sole guidance of CCTA and FFRCT and the Inter-reproducibility of the analysis of the updated score was found excellent. The prognostic value of the modified CCTA-aSS will be examined in future studies.
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Affiliation(s)
- Shigetaka Kageyama
- Department of Cardiology, University of Galway, Galway, Ireland; CORRIB Research Centre for Advanced Imaging and Core Lab, University of Galway, Galway, Ireland
| | - Patrick W Serruys
- Department of Cardiology, University of Galway, Galway, Ireland; CORRIB Research Centre for Advanced Imaging and Core Lab, University of Galway, Galway, Ireland.
| | - Nozomi Kotoku
- Department of Cardiology, University of Galway, Galway, Ireland; CORRIB Research Centre for Advanced Imaging and Core Lab, University of Galway, Galway, Ireland
| | - Scot Garg
- Department of Cardiology, Royal Blackburn Hospital, Blackburn, UK
| | - Kai Ninomiya
- Department of Cardiology, University of Galway, Galway, Ireland; CORRIB Research Centre for Advanced Imaging and Core Lab, University of Galway, Galway, Ireland
| | - Shinichiro Masuda
- Department of Cardiology, University of Galway, Galway, Ireland; CORRIB Research Centre for Advanced Imaging and Core Lab, University of Galway, Galway, Ireland
| | - Marie-Angele Morel
- CORRIB Research Centre for Advanced Imaging and Core Lab, University of Galway, Galway, Ireland
| | | | | | - Brian Thomsen
- Global Research Team, GE Healthcare US, Milwaukee, WI, USA
| | - Gianluca Pontone
- Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Giulio Pompilio
- Unit of Vascular Biology and Regenerative Medicine, Centro Cardiologico Monzino IRCCS, Milano, Italy; Dipartimento di Scienze Cliniche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Milano, Italy
| | - John D Puskas
- Department of Cardiovascular Surgery, Mount Sinai Morningside, New York, NY, USA
| | - Torsten Doenst
- Department of Cardiothoracic Surgery, University Hospital Jena, Jena, Germany
| | - Mark La Meir
- Cardiac Surgery Department, Universitair Ziekenhuis Brussels, VUB, Brussels, Belgium
| | - Ulf Teichgräber
- Department of Radiology, University Hospital Jena, Jena, Germany
| | - Himanshu Gupta
- Cardiac Imaging, Valley Health System, Ridgewood, NJ, USA
| | - Johan De Mey
- Department of Radiology, Universitair Ziekenhuis Brussels, Brussels, Belgium
| | - Daniele Andreini
- Division of Cardiology and Cardiac Imaging, IRCCS Galeazzi Sant'Ambrogio, Milan, Italy; Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Yoshinobu Onuma
- Department of Cardiology, University of Galway, Galway, Ireland
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7
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Rudnicka Z, Pręgowska A, Glądys K, Perkins M, Proniewska K. Advancements in artificial intelligence-driven techniques for interventional cardiology. Cardiol J 2024; 31:321-341. [PMID: 38247435 PMCID: PMC11076027 DOI: 10.5603/cj.98650] [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: 12/22/2023] [Revised: 12/31/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024] Open
Abstract
This paper aims to thoroughly discuss the impact of artificial intelligence (AI) on clinical practice in interventional cardiology (IC) with special recognition of its most recent advancements. Thus, recent years have been exceptionally abundant in advancements in computational tools, including the development of AI. The application of AI development is currently in its early stages, nevertheless new technologies have proven to be a promising concept, particularly considering IC showing great impact on patient safety, risk stratification and outcomes during the whole therapeutic process. The primary goal is to achieve the integration of multiple cardiac imaging modalities, establish online decision support systems and platforms based on augmented and/or virtual realities, and finally to create automatic medical systems, providing electronic health data on patients. In a simplified way, two main areas of AI utilization in IC may be distinguished, namely, virtual and physical. Consequently, numerous studies have provided data regarding AI utilization in terms of automated interpretation and analysis from various cardiac modalities, including electrocardiogram, echocardiography, angiography, cardiac magnetic resonance imaging, and computed tomography as well as data collected during robotic-assisted percutaneous coronary intervention procedures. Thus, this paper aims to thoroughly discuss the impact of AI on clinical practice in IC with special recognition of its most recent advancements.
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Affiliation(s)
- Zofia Rudnicka
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Agnieszka Pręgowska
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Kinga Glądys
- Jagiellonian University Medical College, Krakow, Poland
| | - Mark Perkins
- Collegium Prometricum, the Business School for Healthcare, Sopot, Poland
- Royal Society of Arts, London, United Kingdom
| | - Klaudia Proniewska
- Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Krakow, Poland.
- Center for Digital Medicine and Robotics, Jagiellonian University Medical College, Krakow, Poland.
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8
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Popov M, Amanturdieva A, Zhaksylyk N, Alkanov A, Saniyazbekov A, Aimyshev T, Ismailov E, Bulegenov A, Kuzhukeyev A, Kulanbayeva A, Kalzhanov A, Temenov N, Kolesnikov A, Sakhov O, Fazli S. Dataset for Automatic Region-based Coronary Artery Disease Diagnostics Using X-Ray Angiography Images. Sci Data 2024; 11:20. [PMID: 38172163 PMCID: PMC10764944 DOI: 10.1038/s41597-023-02871-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 12/20/2023] [Indexed: 01/05/2024] Open
Abstract
X-ray coronary angiography is the most common tool for the diagnosis and treatment of coronary artery disease. It involves the injection of contrast agents into coronary vessels using a catheter to highlight the coronary vessel structure. Typically, multiple 2D X-ray projections are recorded from different angles to improve visualization. Recent advances in the development of deep-learning-based tools promise significant improvement in diagnosing and treating coronary artery disease. However, the limited public availability of annotated X-ray coronary angiography image datasets presents a challenge for objective assessment and comparison of existing tools and the development of novel methods. To address this challenge, we introduce a novel ARCADE dataset with 2 objectives: coronary vessel classification and stenosis detection. Each objective contains 1500 expert-labeled X-ray coronary angiography images representing: i) coronary artery segments; and ii) the locations of stenotic plaques. These datasets will serve as a benchmark for developing new methods and assessing existing approaches for the automated diagnosis and risk assessment of coronary artery disease.
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Affiliation(s)
- Maxim Popov
- Mohamed Bin Zayed University of Artificial Intelligence, Department of Computer Vision, Abu Dhabi, United Arab Emirates.
| | - Akmaral Amanturdieva
- Research Institute of Cardiology and Internal Diseases, Almaty, 050000, Kazakhstan
| | - Nuren Zhaksylyk
- Mohamed Bin Zayed University of Artificial Intelligence, Department of Computer Vision, Abu Dhabi, United Arab Emirates
- Research Institute of Cardiology and Internal Diseases, Almaty, 050000, Kazakhstan
| | - Alsabir Alkanov
- Research Institute of Cardiology and Internal Diseases, Almaty, 050000, Kazakhstan
| | - Adilbek Saniyazbekov
- Research Institute of Cardiology and Internal Diseases, Almaty, 050000, Kazakhstan
| | - Temirgali Aimyshev
- Research Institute of Cardiology and Internal Diseases, Almaty, 050000, Kazakhstan
- CMC Technologies, Astana, 010000, Kazakhstan
| | - Eldar Ismailov
- Research Institute of Cardiology and Internal Diseases, Almaty, 050000, Kazakhstan
- CMC Technologies, Astana, 010000, Kazakhstan
| | - Ablay Bulegenov
- Research Institute of Cardiology and Internal Diseases, Almaty, 050000, Kazakhstan
- CMC Technologies, Astana, 010000, Kazakhstan
| | - Arystan Kuzhukeyev
- Research Institute of Cardiology and Internal Diseases, Almaty, 050000, Kazakhstan
| | - Aizhan Kulanbayeva
- Research Institute of Cardiology and Internal Diseases, Almaty, 050000, Kazakhstan
- Almaty City Cardiological Center, Almaty, 050000, Kazakhstan
| | - Almat Kalzhanov
- Research Institute of Cardiology and Internal Diseases, Almaty, 050000, Kazakhstan
| | - Nurzhan Temenov
- Research Institute of Cardiology and Internal Diseases, Almaty, 050000, Kazakhstan
| | - Alexey Kolesnikov
- Research Institute of Cardiology and Internal Diseases, Almaty, 050000, Kazakhstan
| | - Orazbek Sakhov
- Research Institute of Cardiology and Internal Diseases, Almaty, 050000, Kazakhstan
- Almaty City Cardiological Center, Almaty, 050000, Kazakhstan
| | - Siamac Fazli
- Nazarbayev University, School of Engineering and Digital Sciences, Department of Computer Science, Astana, 010000, Kazakhstan
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Nobre Menezes M, Silva B, Silva JL, Rodrigues T, Marques JS, Guerreiro C, Guedes JP, Oliveira-Santos M, Oliveira AL, Pinto FJ. Segmentation of X-ray coronary angiography with an artificial intelligence deep learning model: Impact in operator visual assessment of coronary stenosis severity. Catheter Cardiovasc Interv 2023; 102:631-640. [PMID: 37579212 DOI: 10.1002/ccd.30805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/01/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND Visual assessment of the percentage diameter stenosis (%DSVE ) of lesions is essential in coronary angiography (CAG) interpretation. We have previously developed an artificial intelligence (AI) model capable of accurate CAG segmentation. We aim to compare operators' %DSVE in angiography versus AI-segmented images. METHODS Quantitative coronary analysis (QCA) %DS (%DSQCA ) was previously performed in our published validation dataset. Operators were asked to estimate %DSVE of lesions in angiography versus AI-segmented images in separate sessions and differences were assessed using angiography %DSQCA as reference. RESULTS A total of 123 lesions were included. %DSVE was significantly higher in both the angiography (77% ± 20% vs. 56% ± 13%, p < 0.001) and segmentation groups (59% ± 20% vs. 56% ± 13%, p < 0.001), with a much smaller absolute %DS difference in the latter. For lesions with %DSQCA of 50%-70% (60% ± 5%), an even higher discrepancy was found (angiography: 83% ± 13% vs. 60% ± 5%, p < 0.001; segmentation: 63% ± 15% vs. 60% ± 5%, p < 0.001). Similar, less pronounced, findings were observed for %DSQCA < 50% lesions, but not %DSQCA > 70% lesions. Agreement between %DSQCA /%DSVE across %DSQCA strata (<50%, 50%-70%, >70%) was approximately twice in the segmentation group (60.4% vs. 30.1%; p < 0.001). %DSVE inter-operator differences were smaller with segmentation. CONCLUSION %DSVE was much less discrepant with segmentation versus angiography. Overestimation of %DSQCA < 70% lesions with angiography was especially common. Segmentation may reduce %DSVE overestimation and thus unwarranted revascularization.
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Affiliation(s)
- Miguel Nobre Menezes
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
- Departamento de Coração e Vasos, Serviço de Cardiologia, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | - Beatriz Silva
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
- Departamento de Coração e Vasos, Serviço de Cardiologia, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | | | - Tiago Rodrigues
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
- Departamento de Coração e Vasos, Serviço de Cardiologia, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | - João Silva Marques
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
- Departamento de Coração e Vasos, Serviço de Cardiologia, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | - Cláudio Guerreiro
- Department of Cardiology, Centro Hospitalar de Vila Nova de Gaia, Vila Nova de Gaia, Portugal
| | - João Pedro Guedes
- Unidade de Hemodinâmica e Cardiologia de Intervenção, Serviço de Cardiologia, Centro Hospitalar Universitário do Algarve, Hospital de Faro, Faro, Portugal
| | - Manuel Oliveira-Santos
- Unidade de Intervenção Cardiovascular, Serviço de Cardiologia do Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
- Pólo das Ciências da Saúde, Unidade Central, Azinhaga de Santa Comba, Celas, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | | | - Fausto J Pinto
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
- Departamento de Coração e Vasos, Serviço de Cardiologia, CHULN Hospital de Santa Maria, Lisboa, Portugal
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10
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Wu H, Zhao J, Li J, Zeng Y, Wu W, Zhou Z, Wu S, Xu L, Song M, Yu Q, Song Z, Chen L. One-Stage Detection without Segmentation for Multi-Type Coronary Lesions in Angiography Images Using Deep Learning. Diagnostics (Basel) 2023; 13:3011. [PMID: 37761378 PMCID: PMC10528585 DOI: 10.3390/diagnostics13183011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/12/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
Abstract
It is rare to use the one-stage model without segmentation for the automatic detection of coronary lesions. This study sequentially enrolled 200 patients with significant stenoses and occlusions of the right coronary and categorized their angiography images into two angle views: The CRA (cranial) view of 98 patients with 2453 images and the LAO (left anterior oblique) view of 176 patients with 3338 images. Randomization was performed at the patient level to the training set and test set using a 7:3 ratio. YOLOv5 was adopted as the key model for direct detection. Four types of lesions were studied: Local Stenosis (LS), Diffuse Stenosis (DS), Bifurcation Stenosis (BS), and Chronic Total Occlusion (CTO). At the image level, the precision, recall, mAP@0.1, and mAP@0.5 predicted by the model were 0.64, 0.68, 0.66, and 0.49 in the CRA view and 0.68, 0.73, 0.70, and 0.56 in the LAO view, respectively. At the patient level, the precision, recall, and F1scores predicted by the model were 0.52, 0.91, and 0.65 in the CRA view and 0.50, 0.94, and 0.64 in the LAO view, respectively. YOLOv5 performed the best for lesions of CTO and LS at both the image level and the patient level. In conclusion, the one-stage model without segmentation as YOLOv5 is feasible to be used in automatic coronary lesion detection, with the most suitable types of lesions as LS and CTO.
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Affiliation(s)
- Hui Wu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Jing Zhao
- Department of Geriatrics, The Third Medical Center of Chinese PLA General Hospital, Beijing 100039, China
| | - Jiehui Li
- State Key Laboratory of Cardiovascular Disease, Department of Cardiac Surgery, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100037, China
| | - Yan Zeng
- Department of Research Center, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 201807, China
| | - Weiwei Wu
- College of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Zhuhuang Zhou
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Shuicai Wu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Liang Xu
- State Key Laboratory of Cardiovascular Disease, Department of Structural Heart Disease, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100037, China
| | - Min Song
- State Key Laboratory of Cardiovascular Disease, Department of Cardiac Surgery, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100037, China
| | - Qibin Yu
- State Key Laboratory of Cardiovascular Disease, Department of Cardiac Surgery, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100037, China
| | - Ziwei Song
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Lin Chen
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
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Avram R, Olgin JE, Ahmed Z, Verreault-Julien L, Wan A, Barrios J, Abreau S, Wan D, Gonzalez JE, Tardif JC, So DY, Soni K, Tison GH. CathAI: fully automated coronary angiography interpretation and stenosis estimation. NPJ Digit Med 2023; 6:142. [PMID: 37568050 PMCID: PMC10421915 DOI: 10.1038/s41746-023-00880-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
Coronary angiography is the primary procedure for diagnosis and management decisions in coronary artery disease (CAD), but ad-hoc visual assessment of angiograms has high variability. Here we report a fully automated approach to interpret angiographic coronary artery stenosis from standard coronary angiograms. Using 13,843 angiographic studies from 11,972 adult patients at University of California, San Francisco (UCSF), between April 1, 2008 and December 31, 2019, we train neural networks to accomplish four sequential necessary tasks for automatic coronary artery stenosis localization and estimation. Algorithms are internally validated against criterion-standard labels for each task in hold-out test datasets. Algorithms are then externally validated in real-world angiograms from the University of Ottawa Heart Institute (UOHI) and also retrained using quantitative coronary angiography (QCA) data from the Montreal Heart Institute (MHI) core lab. The CathAI system achieves state-of-the-art performance across all tasks on unselected, real-world angiograms. Positive predictive value, sensitivity and F1 score are all ≥90% to identify projection angle and ≥93% for left/right coronary artery angiogram detection. To predict obstructive CAD stenosis (≥70%), CathAI exhibits an AUC of 0.862 (95% CI: 0.843-0.880). In UOHI external validation, CathAI achieves AUC 0.869 (95% CI: 0.830-0.907) to predict obstructive CAD. In the MHI QCA dataset, CathAI achieves an AUC of 0.775 (95%. CI: 0.594-0.955) after retraining. In conclusion, multiple purpose-built neural networks can function in sequence to accomplish automated analysis of real-world angiograms, which could increase standardization and reproducibility in angiographic coronary stenosis assessment.
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Affiliation(s)
- Robert Avram
- Division of Cardiology, Department of Medicine, University of California, San Francisco, Cardiology, 505 Parnassus Avenue, San Francisco, CA, 94143, USA
- Division of Cardiology, Department of Medicine, Montreal Heart Institute - Université de Montréal, 5000 Rue Belanger, Montreal, QC, H1T 1C8, Canada
| | - Jeffrey E Olgin
- Division of Cardiology, Department of Medicine, University of California, San Francisco, Cardiology, 505 Parnassus Avenue, San Francisco, CA, 94143, USA
- Cardiovascular Research Institute, University of California, San Francisco, CA, 94143, USA
| | - Zeeshan Ahmed
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, University of Ottawa, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Louis Verreault-Julien
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, University of Ottawa, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Alvin Wan
- Cardiovascular Research Institute, University of California, San Francisco, CA, 94143, USA
| | - Joshua Barrios
- Division of Cardiology, Department of Medicine, University of California, San Francisco, Cardiology, 505 Parnassus Avenue, San Francisco, CA, 94143, USA
| | - Sean Abreau
- Division of Cardiology, Department of Medicine, University of California, San Francisco, Cardiology, 505 Parnassus Avenue, San Francisco, CA, 94143, USA
| | - Derek Wan
- Department of Electrical Engineering and Computer Science, RISE Lab, University of California, Berkeley, Soda Hall, Berkeley, CA, 94720-1770, USA
| | - Joseph E Gonzalez
- Department of Electrical Engineering and Computer Science, RISE Lab, University of California, Berkeley, Soda Hall, Berkeley, CA, 94720-1770, USA
| | - Jean-Claude Tardif
- Division of Cardiology, Department of Medicine, Montreal Heart Institute - Université de Montréal, 5000 Rue Belanger, Montreal, QC, H1T 1C8, Canada
| | - Derek Y So
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, University of Ottawa, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Krishan Soni
- Division of Cardiology, Department of Medicine, University of California, San Francisco, Cardiology, 505 Parnassus Avenue, San Francisco, CA, 94143, USA
| | - Geoffrey H Tison
- Division of Cardiology, Department of Medicine, University of California, San Francisco, Cardiology, 505 Parnassus Avenue, San Francisco, CA, 94143, USA.
- Cardiovascular Research Institute, University of California, San Francisco, CA, 94143, USA.
- Department of Electrical Engineering and Computer Science, RISE Lab, University of California, Berkeley, Soda Hall, Berkeley, CA, 94720-1770, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, 94158, USA.
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Nobre Menezes M, Silva JL, Silva B, Rodrigues T, Guerreiro C, Guedes JP, Santos MO, Oliveira AL, Pinto FJ. Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2023:10.1007/s10554-023-02839-5. [PMID: 37027105 DOI: 10.1007/s10554-023-02839-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 03/18/2023] [Indexed: 04/08/2023]
Abstract
INTRODUCTION We previously developed an artificial intelligence (AI) model for automatic coronary angiography (CAG) segmentation, using deep learning. To validate this approach, the model was applied to a new dataset and results are reported. METHODS Retrospective selection of patients undergoing CAG and percutaneous coronary intervention or invasive physiology assessment over a one month period from four centers. A single frame was selected from images containing a lesion with a 50-99% stenosis (visual estimation). Automatic Quantitative Coronary Analysis (QCA) was performed with a validated software. Images were then segmented by the AI model. Lesion diameters, area overlap [based on true positive (TP) and true negative (TN) pixels] and a global segmentation score (GSS - 0 -100 points) - previously developed and published - were measured. RESULTS 123 regions of interest from 117 images across 90 patients were included. There were no significant differences between lesion diameter, percentage diameter stenosis and distal border diameter between the original/segmented images. There was a statistically significant albeit minor difference [0,19 mm (0,09-0,28)] regarding proximal border diameter. Overlap accuracy ((TP + TN)/(TP + TN + FP + FN)), sensitivity (TP / (TP + FN)) and Dice Score (2TP / (2TP + FN + FP)) between original/segmented images was 99,9%, 95,1% and 94,8%, respectively. The GSS was 92 (87-96), similar to the previously obtained value in the training dataset. CONCLUSION the AI model was capable of accurate CAG segmentation across multiple performance metrics, when applied to a multicentric validation dataset. This paves the way for future research on its clinical uses.
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Affiliation(s)
- Miguel Nobre Menezes
- Structural and Coronary Heart Disease Unit, Faculdade de Medicina, Cardiovascular Center of the University of Lisbon, Universidade de Lisboa (CCUL@RISE), Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal.
- Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal.
| | - João Lourenço Silva
- INESC-ID / Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | - Beatriz Silva
- Structural and Coronary Heart Disease Unit, Faculdade de Medicina, Cardiovascular Center of the University of Lisbon, Universidade de Lisboa (CCUL@RISE), Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal
- Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal
| | - Tiago Rodrigues
- Structural and Coronary Heart Disease Unit, Faculdade de Medicina, Cardiovascular Center of the University of Lisbon, Universidade de Lisboa (CCUL@RISE), Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal
- Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal
| | | | - João Pedro Guedes
- Unidade de Hemodinâmica e Cardiologia de Intervenção, Serviço de Cardiologia, Centro Hospitalar Universitário do Algarve, Hospital de Faro, Faro, Portugal
| | - Manuel Oliveira Santos
- Unidade de Intervenção Cardiovascular, Serviço de Cardiologia do Centro Hospitalar e Universitário de Coimbra, Praceta Professor Mota Pinto, Coimbra, 3004-561, Portugal
- Faculdade de Medicina da Universidade de Coimbra, R. Larga 2, Coimbra, 3000-370, Portugal
| | - Arlindo L Oliveira
- INESC-ID / Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | - Fausto J Pinto
- Structural and Coronary Heart Disease Unit, Faculdade de Medicina, Cardiovascular Center of the University of Lisbon, Universidade de Lisboa (CCUL@RISE), Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal
- Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Av Prof. Egas Moniz, Lisboa, 1649-028, Portugal
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Role of artificial intelligence and machine learning in interventional cardiology. Curr Probl Cardiol 2023; 48:101698. [PMID: 36921654 DOI: 10.1016/j.cpcardiol.2023.101698] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 03/16/2023]
Abstract
Directed by two decades of technological processes and remodeling, the dynamic quality of healthcare data combined with the progress of computational power has allowed for rapid progress in artificial intelligence (AI). In interventional cardiology, AI has shown potential in providing data interpretation and automated analysis from electrocardiogram (ECG), echocardiography, computed tomography angiography (CTA), magnetic resonance imaging (MRI), and electronic patient data. Clinical decision support has the potential to assist in improving patient safety and making prognostic and diagnostic conjectures in interventional cardiology procedures. Robot-assisted percutaneous coronary intervention (R-PCI), along with functional and quantitative assessment of coronary artery ischemia and plaque burden on intravascular ultrasound (IVUS), are the major applications of AI. Machine learning (ML) algorithms are used in these applications, and they have the potential to bring a paradigm shift in intervention. Recently, an efficient branch of ML has emerged as a deep learning algorithm for numerous cardiovascular (CV) applications. However, the impact DL on the future of cardiology practice is not clear. Predictive models based on DL have several limitations including low generalizability and decision processing in cardiac anatomy.
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Zhang H, Gao Z, Zhang D, Hau WK, Zhang H. Progressive Perception Learning for Main Coronary Segmentation in X-Ray Angiography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:864-879. [PMID: 36327189 DOI: 10.1109/tmi.2022.3219126] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Main coronary segmentation from the X-ray angiography images is important for the computer-aided diagnosis and treatment of coronary disease. However, it confronts the challenge at three different image granularities (the semantic, surrounding, and local levels). The challenge includes the semantic confusion between the main and collateral vessels, low contrast between the foreground vessel and background surroundings, and local ambiguity near the vessel boundaries. The traditional hand-crafted feature-based methods may be insufficient because they may lack the semantic relationship information and may not distinguish the main and collateral vessels. The existing deep learning-based methods seem to have issues due to the deficiency in the long-distance semantic relationship capture, the foreground and background interference adaptability, and the boundary detail information preservation. To solve the main coronary segmentation challenge, we propose the progressive perception learning (PPL) framework to inspect these three different image granularities. Specifically, the PPL contains the context, interference, and boundary perception modules. The context perception is designed to focus on the main coronary vessel based on the semantic dependence capture among different coronary segments. The interference perception is designed to purify the feature maps based on the foreground vessel enhancement and background artifact suppression. The boundary perception is designed to highlight the boundary details based on boundary feature extraction through the intersection between the foreground and background predictions. Extensive experiments on 1085 subjects show that the PPL is effective (e.g., the overall Dice is greater than 95%), and superior to thirteen state-of-the-art coronary segmentation methods.
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Chu M, Wu P, Li G, Yang W, Gutiérrez-Chico JL, Tu S. Advances in Diagnosis, Therapy, and Prognosis of Coronary Artery Disease Powered by Deep Learning Algorithms. JACC. ASIA 2023; 3:1-14. [PMID: 36873752 PMCID: PMC9982227 DOI: 10.1016/j.jacasi.2022.12.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/29/2022] [Accepted: 12/01/2022] [Indexed: 02/17/2023]
Abstract
Percutaneous coronary intervention has been a standard treatment strategy for patients with coronary artery disease with continuous ebullient progress in technology and techniques. The application of artificial intelligence and deep learning in particular is currently boosting the development of interventional solutions, improving the efficiency and objectivity of diagnosis and treatment. The ever-growing amount of data and computing power together with cutting-edge algorithms pave the way for the integration of deep learning into clinical practice, which has revolutionized the interventional workflow in imaging processing, interpretation, and navigation. This review discusses the development of deep learning algorithms and their corresponding evaluation metrics together with their clinical applications. Advanced deep learning algorithms create new opportunities for precise diagnosis and tailored treatment with a high degree of automation, reduced radiation, and enhanced risk stratification. Generalization, interpretability, and regulatory issues are remaining challenges that need to be addressed through joint efforts from multidisciplinary community.
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Affiliation(s)
- Miao Chu
- Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Peng Wu
- Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Guanyu Li
- Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.,Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark
| | - Wei Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | | | - Shengxian Tu
- Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Zhong Z, Yang W, Zhu C, Wang Z. Role and progress of artificial intelligence in radiodiagnosing vascular calcification: a narrative review. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:131. [PMID: 36819510 PMCID: PMC9929846 DOI: 10.21037/atm-22-6333] [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: 11/17/2022] [Accepted: 01/10/2023] [Indexed: 01/15/2023]
Abstract
Background and Objective Vascular calcification has important clinical significance due to its vital prognostic value for cardiovascular diseases, chronic kidney disease (CKD), diabetes, fracture, and other multisystem diseases. Radiology is the main diagnostic method of it, but facing great pressure such as the increasing workload and decreasing working accuracy rate. Therefore, radiology needs to find a way out to better realize the clinical value of vascular calcification. Artificial intelligence (AI) encompasses any algorithm imitating human intelligence. AI has shown great potential in image analysis, such as its high speed and accuracy, becoming the savior of the current situation. In order to promote more rational utilization, the role and progress of AI in this field were reviewed. Methods A search was conducted in PubMed and Web of Science. The key words included "artificial intelligence", "machine learning", "deep learning", and "vascular calcification". The qualitative analysis of literature was achieved through repeated deliberation after refining valuable content. The theme is the role and progress of AI in the diagnostic radiology of vascular calcification. Key Content and Findings Sixty-two articles were included. AI has been applied to the diagnostic radiology of 5 types of vascular calcification, including coronary artery calcification (CAC), thoracic aortic calcification (TAC), abdominal aortic calcification (AAC), carotid artery calcification, and breast artery calcification (BAC). Deep learning (DL), the latest technology in this field has been well applied and satisfactorily performed. Radiologists have been able to achieve efficient diagnosis of 5 types of vascular calcification through AI, with reliable accuracy. Conclusions Increasingly, advanced AI has achieved an accuracy comparable to that of human experts, with a faster speed. Moreover, the ability to reduce noise and artifacts enables more imaging equipment to obtain reliable quantification. AI has acquired the ability to cooperate with radiology departments in future work. However, the research in AAC and carotid artery calcification can be more in-depth, and more types of vascular calcification and more fields of radiology should be expanded to. The interpretation of results made by AI and the promotion of existing achievements to the development of other disciplines are also the focus in future.
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Affiliation(s)
- Zhiqi Zhong
- Department of Cardiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Wenjun Yang
- Department of Cardiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Chengcheng Zhu
- Digestive Endoscopy Center, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Zhongqun Wang
- Department of Cardiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
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Moon IT, Kim SH, Chin JY, Park SH, Yoon CH, Youn TJ, Chae IH, Kang SH. Accuracy of Artificial Intelligence-Based Automated Quantitative Coronary Angiography Compared to Intravascular Ultrasound: Retrospective Cohort Study. (Preprint). JMIR Cardio 2022; 7:e45299. [PMID: 37099368 PMCID: PMC10173041 DOI: 10.2196/45299] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 03/06/2023] [Accepted: 03/14/2023] [Indexed: 03/15/2023] Open
Abstract
BACKGROUND An accurate quantitative analysis of coronary artery stenotic lesions is essential to make optimal clinical decisions. Recent advances in computer vision and machine learning technology have enabled the automated analysis of coronary angiography. OBJECTIVE The aim of this paper is to validate the performance of artificial intelligence-based quantitative coronary angiography (AI-QCA) in comparison with that of intravascular ultrasound (IVUS). METHODS This retrospective study included patients who underwent IVUS-guided coronary intervention at a single tertiary center in Korea. Proximal and distal reference areas, minimal luminal area, percent plaque burden, and lesion length were measured by AI-QCA and human experts using IVUS. First, fully automated QCA analysis was compared with IVUS analysis. Next, we adjusted the proximal and distal margins of AI-QCA to avoid geographic mismatch. Scatter plots, Pearson correlation coefficients, and Bland-Altman were used to analyze the data. RESULTS A total of 54 significant lesions were analyzed in 47 patients. The proximal and distal reference areas, as well as the minimal luminal area, showed moderate to strong correlation between the 2 modalities (correlation coefficients of 0.57, 0.80, and 0.52, respectively; P<.001). The correlation was weaker for percent area stenosis and lesion length, although statistically significant (correlation coefficients of 0.29 and 0.33, respectively). AI-QCA tended to measure reference vessel areas smaller and lesion lengths shorter than IVUS did. Systemic proportional bias was not observed in Bland-Altman plots. The biggest cause of bias originated from the geographic mismatch of AI-QCA with IVUS. Discrepancies in the proximal or distal lesion margins were observed between the 2 modalities, which were more frequent at the distal margins. After the adjustment of proximal or distal margins, there was a stronger correlation of proximal and distal reference areas between AI-QCA and IVUS (correlation coefficients of 0.70 and 0.83, respectively). CONCLUSIONS AI-QCA showed a moderate to strong correlation compared with IVUS in analyzing coronary lesions with significant stenosis. The main discrepancy was in the perception of the distal margins by AI-QCA, and the correction of margins improved the correlation coefficients. We believe that this novel tool could provide confidence to treating physicians and help in making optimal clinical decisions.
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Affiliation(s)
- In Tae Moon
- Uijeongbu Eulji University Hospital, Uijeongbu, Republic of Korea
| | - Sun-Hwa Kim
- Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jung Yeon Chin
- Uijeongbu Eulji University Hospital, Uijeongbu, Republic of Korea
| | - Sung Hun Park
- Uijeongbu Eulji University Hospital, Uijeongbu, Republic of Korea
| | - Chang-Hwan Yoon
- Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Tae-Jin Youn
- Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - In-Ho Chae
- Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Si-Hyuck Kang
- Seoul National University Bundang Hospital, Seongnam, Republic of Korea
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Nobre Menezes M, Lourenço-Silva J, Silva B, Rodrigues O, Francisco ARG, Carrilho Ferreira P, Oliveira AL, Pinto FJ. Development of deep learning segmentation models for coronary X-ray angiography: Quality assessment by a new global segmentation score and comparison with human performance. Rev Port Cardiol 2022; 41:1011-1021. [PMID: 36511271 DOI: 10.1016/j.repc.2022.04.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 04/03/2022] [Indexed: 01/11/2023] Open
Abstract
INTRODUCTION AND OBJECTIVES Although automatic artificial intelligence (AI) coronary angiography (CAG) segmentation is arguably the first step toward future clinical application, it is underexplored. We aimed to (1) develop AI models for CAG segmentation and (2) assess the results using similarity scores and a set of criteria defined by expert physicians. METHODS Patients undergoing CAG were randomly selected in a retrospective study at a single center. Per incidence, an ideal frame was segmented, forming a baseline human dataset (BH), used for training a baseline AI model (BAI). Enhanced human segmentation (EH) was created by combining the best of both. An enhanced AI model (EAI) was trained using the EH. Results were assessed by experts using 11 weighted criteria, combined into a Global Segmentation Score (GSS: 0-100 points). Generalized Dice Score (GDS) and Dice Similarity Coefficient (DSC) were also used for AI models assessment. RESULTS 1664 processed images were generated. GSS for BH, EH, BAI and EAI were 96.9+/-5.7; 98.9+/-3.1; 86.1+/-10.1 and 90+/-7.6, respectively (95% confidence interval, p<0.001 for both paired and global differences). The GDS for the BAI and EAI was 0.9234±0.0361 and 0.9348±0.0284, respectively. The DSC for the coronary tree was 0.8904±0.0464 and 0.9134±0.0410 for the BAI and EAI, respectively. The EAI outperformed the BAI in all coronary segmentation tasks, but performed less well in some catheter segmentation tasks. CONCLUSIONS We successfully developed AI models capable of CAG segmentation, with good performance as assessed by all scores.
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Affiliation(s)
- Miguel Nobre Menezes
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal.
| | | | - Beatriz Silva
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | - Oliveira Rodrigues
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | - Ana Rita G Francisco
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | - Pedro Carrilho Ferreira
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal
| | | | - Fausto J Pinto
- Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal
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19
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Alskaf E, Dutta U, Scannell CM, Chiribiri A. Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis. JOURNAL OF MEDICAL ARTIFICIAL INTELLIGENCE 2022; 5:11. [PMID: 36861064 PMCID: PMC7614252 DOI: 10.21037/jmai-22-36] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Background The application of deep learning on medical imaging is growing in prevalence in the recent literature. One of the most studied areas is coronary artery disease (CAD). Imaging of coronary artery anatomy is fundamental, which has led to a high number of publications describing a variety of techniques. The aim of this systematic review is to review the evidence behind the accuracy of deep learning applications in coronary anatomy imaging. Methods The search for the relevant studies, which applied deep learning on coronary anatomy imaging, was performed in a systematic approach on MEDLINE and EMBASE databases, followed by reviewing of abstracts and full texts. The data from the final studies was retrieved using data extraction forms. A meta-analysis was performed on a subgroup of studies, which looked at fractional flow reserve (FFR) prediction. Heterogeneity was tested using tau2, I2 and Q tests. Finally, a risk of bias was performed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) approach. Results A total of 81 studies met the inclusion criteria. The most common imaging modality was coronary computed tomography angiography (CCTA) (58%) and the most common deep learning method was convolutional neural network (CNN) (52%). The majority of studies demonstrated good performance metrics. The most common outputs were focused on coronary artery segmentation, clinical outcome prediction, coronary calcium quantification and FFR prediction, and most studies reported area under the curve (AUC) of ≥80%. The pooled diagnostic odds ratio (DOR) derived from 8 studies looking at FFR prediction using CCTA was 12.5 using the Mantel-Haenszel (MH) method. There was no significant heterogeneity amongst studies according to Q test (P=0.2496). Conclusions Deep learning has been used in many applications on coronary anatomy imaging, most of which are yet to be externally validated and prepared for clinical use. The performance of deep learning, especially CNN models, proved to be powerful and some applications have already translated into medical practice, such as computed tomography (CT)-FFR. These applications have the potential to translate technology into better care of CAD patients.
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Affiliation(s)
- Ebraham Alskaf
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Utkarsh Dutta
- GKT School of Medical Education, King’s College London, London, UK
| | - Cian M. Scannell
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK,Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Amedeo Chiribiri
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
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20
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A deep learning method to assist with chronic atrophic gastritis diagnosis using white light images. Dig Liver Dis 2022; 54:1513-1519. [PMID: 35610166 DOI: 10.1016/j.dld.2022.04.025] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/29/2022] [Accepted: 04/20/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND Chronic atrophic gastritis is a common preneoplastic condition of the stomach with a low detection rate during endoscopy. AIMS This study aimed to develop two deep learning models to improve the diagnostic rate. METHODS We collected 10,593 images from 4005 patients including 2280 patients with chronic atrophic gastritis and 1725 patients with chronic non-atrophic gastritis from two tertiary hospitals. Two deep learning models were developed to detect chronic atrophic gastritis using ResNet50. The detection ability of the deep learning model was compared with that of three expert endoscopists. RESULTS In the external test set, the diagnostic accuracy of model 1 for detecting gastric antrum atrophy was 0.890. The identification accuracies for the severity of gastric antrum atrophy were 0.773 and 0.590 in the internal and external test sets, respectively. In the other two external sets, the detection accuracies of model 2 for chronic atrophic gastritis were 0.854 and 0.916, respectively. Deep learning model 1's ability to identify gastric antrum atrophy was comparable to that of human experts. CONCLUSION Deep-learning-based models can detect chronic atrophic gastritis with good performance, which may greatly reduce the burden on endoscopists, relieve patient suffering, and improve the disease's detection rate in primary hospitals.
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21
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Langlais ÉL, Thériault-Lauzier P, Marquis-Gravel G, Kulbay M, So DY, Tanguay JF, Ly HQ, Gallo R, Lesage F, Avram R. Novel Artificial Intelligence Applications in Cardiology: Current Landscape, Limitations, and the Road to Real-World Applications. J Cardiovasc Transl Res 2022:10.1007/s12265-022-10260-x. [PMID: 35460017 DOI: 10.1007/s12265-022-10260-x] [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: 02/21/2022] [Accepted: 04/11/2022] [Indexed: 11/25/2022]
Abstract
Cardiovascular diseases are the leading cause of death globally and contribute significantly to the cost of healthcare. Artificial intelligence (AI) is poised to reshape cardiology. Using supervised and unsupervised learning, the two main branches of AI, several applications have been developed in recent years to improve risk prediction, allow large-scale analysis of medical data, and phenotype patients for personalized medicine. In this review, we examine the key advances in AI in cardiology and its limitations regarding bias in the data, standardization in reporting, data access, and model trust and accountability in cases of error. Finally, we discuss implementation methods to unleash AI's potential in making healthcare more accurate and efficient. Several steps need to be followed and challenges overcome in order to successfully integrate AI in clinical practice and ensure its longevity.
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Affiliation(s)
- Élodie Labrecque Langlais
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
- Biomedical Engineering, École Polytechnique de Montréal, 2500 Chemin de Polytechnique, Montreal, QC, H3T 1J4, Canada
| | - Pascal Thériault-Lauzier
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, University of Ottawa, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Guillaume Marquis-Gravel
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
- Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada
| | - Merve Kulbay
- Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada
| | - Derek Y So
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, University of Ottawa, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada
| | - Jean-François Tanguay
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
| | - Hung Q Ly
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
| | - Richard Gallo
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
- Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada
| | - Frédéric Lesage
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada
- Biomedical Engineering, École Polytechnique de Montréal, 2500 Chemin de Polytechnique, Montreal, QC, H3T 1J4, Canada
| | - Robert Avram
- Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, 5000 Belanger Street, Montreal, QC, H1T 1C8, Canada.
- Department of Medicine, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC, H3T 1J4, Canada.
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22
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Liu MH, Zhao C, Wang S, Jia H, Yu B. Artificial Intelligence—A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome. Front Cardiovasc Med 2022; 8:782971. [PMID: 35252367 PMCID: PMC8888682 DOI: 10.3389/fcvm.2021.782971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 12/29/2021] [Indexed: 11/19/2022] Open
Abstract
Acute coronary syndrome is the leading cause of cardiac death and has a significant impact on patient prognosis. Early identification and proper management are key to ensuring better outcomes and have improved significantly with the development of various cardiovascular imaging modalities. Recently, the use of artificial intelligence as a method of enhancing the capability of cardiovascular imaging has grown. AI can inform the decision-making process, as it enables existing modalities to perform more efficiently and make more accurate diagnoses. This review demonstrates recent applications of AI in cardiovascular imaging to facilitate better patient care.
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Affiliation(s)
- Ming-hao Liu
- Department of Cardiology, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China
- The Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin, China
| | - Chen Zhao
- Department of Cardiology, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China
- The Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin, China
| | - Shengfang Wang
- Department of Cardiology, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China
- The Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin, China
| | - Haibo Jia
- Department of Cardiology, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China
- The Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin, China
- *Correspondence: Haibo Jia
| | - Bo Yu
- Department of Cardiology, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China
- The Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin, China
- Bo Yu
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23
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Liu X, Huang Y, Xie L, Wang X, Guan C, Du T, Chen D, Zou T, Shi Z, Li A, Zhao S, Xu Y, Zhang H, Xu B. Automatic construction of coronary artery tree structure based on vessel blood flow tracking. Catheter Cardiovasc Interv 2022; 99 Suppl 1:1378-1385. [PMID: 35077599 DOI: 10.1002/ccd.30061] [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: 11/17/2021] [Accepted: 12/20/2021] [Indexed: 11/07/2022]
Abstract
We sought to propose an innovative vessel blood flow tracking (VBFT) method to extract coronary artery tree (CAT) and to assess the effectiveness of this VBFT versus the single-frame method. Construction of a CAT from a segmented artery is the basis of artificial intelligence-aided angiographic diagnosis. However, construction of a CAT using a single frame remains challenging, due to bifurcations and overlaps in two-dimensional angiograms. Overall, 13,222 angiograms, including 28,539 vessels, were retrospectively collected from 3275 patients and were then annotated. Coronary arteries were automatically segmented by a previously established deep neural networks (DNNs), and the skeleton lines were then extracted from segmentation images to construct CAT using the single-frame method and the VBFT method. Additionally, 1322 angiograms with 2201 vessels were used to test these two methods. Compared to the single-frame method, the VBFT method can significantly improve the accuracy of CAT as (84.3% vs. 72.3%; p < 0.001). Overlap (OV) was higher in the VBFT group than that in the Single-Frame group (91.1% vs. 87.5%; p < 0.001). The VBFT method significantly reduced the incidence of the lack of branching (7.30% vs. 13.9%, p < 0.001), insufficient length (6.70% vs. 11.0%, p < 0.001), and redundant branches (1.60% vs. 3.10%, p < 0.001). The VBFT method improved the extraction of a CAT structure, which will facilitate the development of artificial intelligence-aided angiographic diagnosis. Cardiologists can efficiently diagnose CAD using this method.
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Affiliation(s)
- Xuqing Liu
- Beijing University of Posts and Telecommunications, Beijing, China
| | - Yunfei Huang
- Catheterization Laboratories, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Lihua Xie
- Catheterization Laboratories, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaofei Wang
- Beijing University of Posts and Telecommunications, Beijing, China
| | - Changdong Guan
- Catheterization Laboratories, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Tianming Du
- Beijing University of Posts and Telecommunications, Beijing, China
| | - Donghao Chen
- Beijing University of Posts and Telecommunications, Beijing, China
| | - Tongqiang Zou
- Catheterization Laboratories, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Zhenpeng Shi
- Catheterization Laboratories, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Ang Li
- Catheterization Laboratories, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | | | - Yang Xu
- Beijing Redcdn Technology Co., Ltd, Beijing, China
| | - Honggang Zhang
- Beijing University of Posts and Telecommunications, Beijing, China
| | - Bo Xu
- Catheterization Laboratories, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Beijing, China.,National Clinical Research Center for Cardiovascular Diseases, Beijing, China
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24
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Makimoto H. Artificial Intelligence in Medicine (AIM) in Cardiovascular Disorders. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_170] [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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25
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Ben Ali W, Pesaranghader A, Avram R, Overtchouk P, Perrin N, Laffite S, Cartier R, Ibrahim R, Modine T, Hussin JG. Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble. Front Cardiovasc Med 2021; 8:711401. [PMID: 34957230 PMCID: PMC8692711 DOI: 10.3389/fcvm.2021.711401] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 11/08/2021] [Indexed: 12/23/2022] Open
Abstract
Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep Learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.
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Affiliation(s)
- Walid Ben Ali
- Service Médico-Chirurgical, Valvulopathies-Chirurgie Cardiaque-Cardiologie Interventionelle Structurelle, Hôpital Cardiologique de Haut Lévèque, Bordeaux, France.,Structural Heart Program and Interventional Cardiology, Université de Montréal, Montreal Heart Institute, Montréal, QC, Canada
| | - Ahmad Pesaranghader
- Faculty of Medicine, Research Center, Montreal Heart Institute, Université de Montréal, Montréal, QC, Canada.,Computer Science and Operations Research Department, Mila (Quebec Artificial Intelligence Institute), Montreal, QC, Canada
| | - Robert Avram
- Faculty of Medicine, Research Center, Montreal Heart Institute, Université de Montréal, Montréal, QC, Canada
| | - Pavel Overtchouk
- Interventional Cardiology and Cardiovascular Surgery Centre Hospitalier Regional Universitaire de Lille (CHRU de Lille), Lille, France
| | - Nils Perrin
- Structural Heart Program and Interventional Cardiology, Université de Montréal, Montreal Heart Institute, Montréal, QC, Canada
| | - Stéphane Laffite
- Service Médico-Chirurgical, Valvulopathies-Chirurgie Cardiaque-Cardiologie Interventionelle Structurelle, Hôpital Cardiologique de Haut Lévèque, Bordeaux, France
| | - Raymond Cartier
- Structural Heart Program and Interventional Cardiology, Université de Montréal, Montreal Heart Institute, Montréal, QC, Canada
| | - Reda Ibrahim
- Structural Heart Program and Interventional Cardiology, Université de Montréal, Montreal Heart Institute, Montréal, QC, Canada
| | - Thomas Modine
- Service Médico-Chirurgical, Valvulopathies-Chirurgie Cardiaque-Cardiologie Interventionelle Structurelle, Hôpital Cardiologique de Haut Lévèque, Bordeaux, France
| | - Julie G Hussin
- Faculty of Medicine, Research Center, Montreal Heart Institute, Université de Montréal, Montréal, QC, Canada
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26
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Howard J, Reiber J. Automated analysis of coronary angiograms using artificial intelligence: a window into the cath lab of the future. EUROINTERVENTION 2021; 17:16-17. [PMID: 33998524 PMCID: PMC9707463 DOI: 10.4244/eijv17i1a2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
- James Howard
- National Heart and Lung Institute, Imperial College London , London, United Kingdom
| | - Johan Reiber
- Leiden University Medical Center, Department of Radiology, Albinusdreef 2, 2333 ZA Leiden, the Netherlands
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27
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Artificial Intelligence in Medicine (AIM) in Cardiovascular Disorders. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_170-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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