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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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Chou CC, Liu ZY, Chang PC, Liu HT, Wo HT, Lee WC, Wang CC, Chen JS, Kuo CF, Wen MS. Comparing Artificial Intelligence-Enabled Electrocardiogram Models in Identifying Left Atrium Enlargement and Long-term Cardiovascular Risk. Can J Cardiol 2024; 40:585-594. [PMID: 38163477 DOI: 10.1016/j.cjca.2023.12.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 10/24/2023] [Accepted: 12/24/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND The role of P-wave in identifying left atrial enlargement (LAE) with the use of artificial intelligence (AI)-enabled electrocardiography (ECG) models is unclear. It is also unknown if AI-enabled single-lead ECG could be used as a diagnostic tool for LAE surveillance. We aimed to build AI-enabled P-wave and single-lead ECG models to identify LAE using sinus rhythm (SR) and non-SR ECGs, and compare the prognostic ability of severe LAE, defined as left atrial diameter ≥ 50 mm, assessed by AI-enabled ECG models vs echocardiography. METHODS This retrospective study used data from 382,594 consecutive adults with paired 12-lead ECG and echocardiography performed within 2 weeks of each other at Chang Gung Memorial Hospital. UNet++ was used for P-wave segmentation. ResNet-18 was used to develop deep convolutional neural network-enabled ECG models for discriminating LAE. External validation was performed with the use of data from 11,753 patients from another hospital. RESULTS The AI-enabled 12-lead ECG model outperformed other ECG models for classifying LAE, but the single-lead ECG models also showed excellent performance at a left atrial diameter cutoff of 50 mm. AI-enabled ECG models had excellent and fair discrimination on LAE using the SR and the non-SR data set, respectively. Severe LAE identified by AI-enabled ECG models was more predictive of future cardiovascular disease than echocardiography; however, the cumulative incidence of new-onset atrial fibrillation and heart failure was higher in patients with echocardiography-severe LAE than with AI-enabled ECG-severe LAE. CONCLUSIONS P-Wave plays a crucial role in discriminating LAE in AI-enabled ECG models. AI-enabled ECG models outperform echocardiography in predicting new-onset cardiovascular diseases associated with severe LAE.
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Affiliation(s)
- Chung-Chuan Chou
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Zhi-Yong Liu
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Po-Cheng Chang
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Hao-Tien Liu
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan
| | - Hung-Ta Wo
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan
| | - Wen-Chen Lee
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan
| | - Chun-Chieh Wang
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Jung-Sheng Chen
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Chang-Fu Kuo
- School of Medicine, Chang Gung University, Taoyuan, Taiwan; Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Division of Rheumatology, Allergy, and Immunology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan.
| | - Ming-Shien Wen
- Division of Cardiology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan.
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Jacquemyn X, Kutty S, Manlhiot C. The Lifelong Impact of Artificial Intelligence and Clinical Prediction Models on Patients With Tetralogy of Fallot. CJC PEDIATRIC AND CONGENITAL HEART DISEASE 2023; 2:440-452. [PMID: 38161675 PMCID: PMC10755786 DOI: 10.1016/j.cjcpc.2023.08.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 08/24/2023] [Indexed: 01/03/2024]
Abstract
Medical advancements in the diagnosis, surgical techniques, perioperative care, and continued care throughout childhood have transformed the outlook for individuals with tetralogy of Fallot (TOF), improving survival and shifting the perspective towards lifelong care. However, with a growing population of survivors, longstanding challenges have been accentuated, and new challenges have surfaced, necessitating a re-evaluation of TOF care. Availability of prenatal diagnostics, insufficient information from traditional imaging techniques, previously unforeseen medical complications, and debates surrounding optimal timing and indications for reintervention are among the emerging issues. To address these challenges, the integration of artificial intelligence and machine learning holds great promise as they have the potential to revolutionize patient management and positively impact lifelong outcomes for individuals with TOF. Innovative applications of artificial intelligence and machine learning have spanned across multiple domains of TOF care, including screening and diagnosis, automated image processing and interpretation, clinical risk stratification, and planning and performing cardiac interventions. By embracing these advancements and incorporating them into routine clinical practice, personalized medicine could be delivered, leading to the best possible outcomes for patients. In this review, we provide an overview of these evolving applications and emphasize the challenges, limitations, and future potential for integrating them into clinical care.
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Affiliation(s)
- Xander Jacquemyn
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Shelby Kutty
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Cedric Manlhiot
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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Beaudoin J, Sénéchal M. Echocardiographic evaluation of ejection fraction by auto-AFI: faster and better? THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2022; 38:1503-1504. [PMID: 38819548 DOI: 10.1007/s10554-022-02582-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 02/24/2022] [Indexed: 11/05/2022]
Affiliation(s)
- Jonathan Beaudoin
- Department of Cardiology, Institut Universitaire de Pneumologie et de Cardiologie de Québec, Laval University, 2725, G1V 4G5, Quebec, Canada
| | - Mario Sénéchal
- Department of Cardiology, Institut Universitaire de Pneumologie et de Cardiologie de Québec, Laval University, 2725, G1V 4G5, Quebec, Canada.
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Lauzier PT, Chow BJW. Artificial intelligence detection of left ventricular systolic dysfunction using chest X-rays: Prospective validation please. Can J Cardiol 2022; 38:720-722. [PMID: 35181459 DOI: 10.1016/j.cjca.2022.02.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/10/2022] [Accepted: 02/11/2022] [Indexed: 11/02/2022] Open
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Accuracy of Deep Learning Echocardiographic View Classification in Patients with Congenital or Structural Heart Disease: Importance of Specific Datasets. J Clin Med 2022; 11:jcm11030690. [PMID: 35160148 PMCID: PMC8836991 DOI: 10.3390/jcm11030690] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/21/2022] [Accepted: 01/26/2022] [Indexed: 12/10/2022] Open
Abstract
Introduction: Automated echocardiography image interpretation has the potential to transform clinical practice. However, neural networks developed in general cohorts may underperform in the setting of altered cardiac anatomy. Methods: Consecutive echocardiographic studies of patients with congenital or structural heart disease (C/SHD) were used to validate an existing convolutional neural network trained on 14,035 echocardiograms for automated view classification. In addition, a new convolutional neural network for view classification was trained and tested specifically in patients with C/SHD. Results: Overall, 9793 imaging files from 262 patients with C/SHD (mean age 49 years, 60% male) and 62 normal controls (mean age 45 years, 50.0% male) were included. Congenital diagnoses included among others, tetralogy of Fallot (30), Ebstein anomaly (18) and transposition of the great arteries (TGA, 48). Assessing correct view classification based on 284,250 individual frames revealed that the non-congenital model had an overall accuracy of 48.3% for correct view classification in patients with C/SHD compared to 66.7% in patients without cardiac disease. Our newly trained convolutional network for echocardiographic view detection based on over 139,910 frames and tested on 35,614 frames from C/SHD patients achieved an accuracy of 76.1% in detecting the correct echocardiographic view. Conclusions: The current study is the first to validate view classification by neural networks in C/SHD patients. While generic models have acceptable accuracy in general cardiology patients, the quality of image classification is only modest in patients with C/SHD. In contrast, our model trained in C/SHD achieved a considerably increased accuracy in this particular cohort.
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Nattel S. Digital Technologies: Revolutionizing Cardiovascular Medicine and Reshaping the World. Can J Cardiol 2021; 38:142-144. [PMID: 34954008 DOI: 10.1016/j.cjca.2021.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 12/19/2021] [Indexed: 11/26/2022] Open
Affiliation(s)
- Stanley Nattel
- Department of Medicine and Research Center, Montreal Heart Institute and Université de Montréal, Montreal, Quebec, Canada; Institute of Pharmacology, West German Heart and Vascular Center, University Duisburg-Essen, Germany; IHU LIRYC and Fondation Bordeaux Université, Bordeaux, France.
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