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Mooghali M, Stroud AM, Yoo DW, Barry BA, Grimshaw AA, Ross JS, Zhu X, Miller JE. Trustworthy and ethical AI-enabled cardiovascular care: a rapid review. BMC Med Inform Decis Mak 2024; 24:247. [PMID: 39232725 PMCID: PMC11373417 DOI: 10.1186/s12911-024-02653-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 08/26/2024] [Indexed: 09/06/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) is increasingly used for prevention, diagnosis, monitoring, and treatment of cardiovascular diseases. Despite the potential for AI to improve care, ethical concerns and mistrust in AI-enabled healthcare exist among the public and medical community. Given the rapid and transformative recent growth of AI in cardiovascular care, to inform practice guidelines and regulatory policies that facilitate ethical and trustworthy use of AI in medicine, we conducted a literature review to identify key ethical and trust barriers and facilitators from patients' and healthcare providers' perspectives when using AI in cardiovascular care. METHODS In this rapid literature review, we searched six bibliographic databases to identify publications discussing transparency, trust, or ethical concerns (outcomes of interest) associated with AI-based medical devices (interventions of interest) in the context of cardiovascular care from patients', caregivers', or healthcare providers' perspectives. The search was completed on May 24, 2022 and was not limited by date or study design. RESULTS After reviewing 7,925 papers from six databases and 3,603 papers identified through citation chasing, 145 articles were included. Key ethical concerns included privacy, security, or confidentiality issues (n = 59, 40.7%); risk of healthcare inequity or disparity (n = 36, 24.8%); risk of patient harm (n = 24, 16.6%); accountability and responsibility concerns (n = 19, 13.1%); problematic informed consent and potential loss of patient autonomy (n = 17, 11.7%); and issues related to data ownership (n = 11, 7.6%). Major trust barriers included data privacy and security concerns, potential risk of patient harm, perceived lack of transparency about AI-enabled medical devices, concerns about AI replacing human aspects of care, concerns about prioritizing profits over patients' interests, and lack of robust evidence related to the accuracy and limitations of AI-based medical devices. Ethical and trust facilitators included ensuring data privacy and data validation, conducting clinical trials in diverse cohorts, providing appropriate training and resources to patients and healthcare providers and improving their engagement in different phases of AI implementation, and establishing further regulatory oversights. CONCLUSION This review revealed key ethical concerns and barriers and facilitators of trust in AI-enabled medical devices from patients' and healthcare providers' perspectives. Successful integration of AI into cardiovascular care necessitates implementation of mitigation strategies. These strategies should focus on enhanced regulatory oversight on the use of patient data and promoting transparency around the use of AI in patient care.
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Affiliation(s)
- Maryam Mooghali
- Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
- Yale Center for Outcomes Research and Evaluation (CORE), 195 Church Street, New Haven, CT, 06510, USA.
| | - Austin M Stroud
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN, USA
| | - Dong Whi Yoo
- School of Information, Kent State University, Kent, OH, USA
| | - Barbara A Barry
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
- Division of Health Care Delivery Research, Mayo Clinic, Rochester, MN, USA
| | - Alyssa A Grimshaw
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, New Haven, CT, USA
| | - Joseph S Ross
- Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Xuan Zhu
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Jennifer E Miller
- Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
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Naderian S, Nikniaz Z, Farhangi MA, Nikniaz L, Sama-Soltani T, Rostami P. Predicting dyslipidemia incidence: unleashing machine learning algorithms on Lifestyle Promotion Project data. BMC Public Health 2024; 24:1777. [PMID: 38961394 PMCID: PMC11223414 DOI: 10.1186/s12889-024-19261-8] [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: 12/13/2023] [Accepted: 06/25/2024] [Indexed: 07/05/2024] Open
Abstract
BACKGROUND Dyslipidemia, characterized by variations in plasma lipid profiles, poses a global health threat linked to millions of deaths annually. OBJECTIVES This study focuses on predicting dyslipidemia incidence using machine learning methods, addressing the crucial need for early identification and intervention. METHODS The dataset, derived from the Lifestyle Promotion Project (LPP) in East Azerbaijan Province, Iran, undergoes a comprehensive preprocessing, merging, and null handling process. Target selection involves five distinct dyslipidemia-related variables. Normalization techniques and three feature selection algorithms are applied to enhance predictive modeling. RESULT The study results underscore the potential of different machine learning algorithms, specifically multi-layer perceptron neural network (MLP), in reaching higher performance metrics such as accuracy, F1 score, sensitivity and specificity, among other machine learning methods. Among other algorithms, Random Forest also showed remarkable accuracies and outperformed K-Nearest Neighbors (KNN) in metrics like precision, recall, and F1 score. The study's emphasis on feature selection detected meaningful patterns among five target variables related to dyslipidemia, indicating fundamental shared unities among dyslipidemia-related factors. Features such as waist circumference, serum vitamin D, blood pressure, sex, age, diabetes, and physical activity related to dyslipidemia. CONCLUSION These results cooperatively highlight the complex nature of dyslipidemia and its connections with numerous factors, strengthening the importance of applying machine learning methods to understand and predict its incidence precisely.
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Affiliation(s)
- Senobar Naderian
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Zeinab Nikniaz
- Liver and Gastrointestinal Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | - Leila Nikniaz
- Tabriz Health Services Management Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
| | - Taha Sama-Soltani
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
| | - Parisa Rostami
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
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Parchmann N, Hansen D, Orzechowski M, Steger F. An ethical assessment of professional opinions on concerns, chances, and limitations of the implementation of an artificial intelligence-based technology into the geriatric patient treatment and continuity of care. GeroScience 2024:10.1007/s11357-024-01229-6. [PMID: 38834930 DOI: 10.1007/s11357-024-01229-6] [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: 04/05/2024] [Accepted: 05/27/2024] [Indexed: 06/06/2024] Open
Abstract
With the introduction of an artificial intelligence-based dashboard into the clinic, the project SURGE-Ahead responds to the importance of improving perioperative geriatric patient treatment and continuity of care. The use of artificial intelligence to process and analyze data automatically, aims at an evidence-based evaluation of the patient's health condition and recommending treatment options. However, its development and introduction raise ethical questions. To ascertain professional perspectives on the clinical use of the dashboard, we have conducted 19 semi-structured qualitative interviews with head physicians, computer scientists, jurists, and ethicists. The application of a qualitative content analysis and thematic analysis enabled the detection of main ethical concerns, chances, and limitations. These ethical considerations were categorized: changes of the patient-physician relationship and the current social reality are expected, causing de-skilling and an active participation of the artificial intelligence. The interviewees anticipated a redistribution of human resources, time, knowledge, and experiences as well as expenses and financing. Concerns of privacy, accuracy, transparency, and explainability were stated, and an insufficient data basis, an intensifying of existing inequalities and systematic discrimination considering a fair access emphasized. Concluding, the patient-physician relationship, social reality, redistribution of resources, fair access, as well as data-related aspects of the artificial intelligence-based system could conflict with the ethical principles of autonomy, non-maleficence, beneficence, and social justice. To respond to these ethical concerns, a responsible use of the dashboard and a critical verification of therapy suggestions is mandatory, and the application limited by questions at the end of life and taking life-changing decisions.
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Affiliation(s)
- Nina Parchmann
- Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Oberberghof 7, 89081, Ulm, Baden-Wuerttemberg, Germany.
| | - David Hansen
- Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Oberberghof 7, 89081, Ulm, Baden-Wuerttemberg, Germany
| | - Marcin Orzechowski
- Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Oberberghof 7, 89081, Ulm, Baden-Wuerttemberg, Germany
| | - Florian Steger
- Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Oberberghof 7, 89081, Ulm, Baden-Wuerttemberg, Germany
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Ose B, Sattar Z, Gupta A, Toquica C, Harvey C, Noheria A. Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review. Curr Cardiol Rep 2024; 26:561-580. [PMID: 38753291 DOI: 10.1007/s11886-024-02062-1] [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] [Accepted: 04/17/2024] [Indexed: 06/26/2024]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovascular screening in the population. AI can be applied to the standard 12-lead resting ECG and single-lead ECGs in external monitors, implantable devices, and direct-to-consumer smart devices. We summarize the current state of the literature on AI-ECG. RECENT FINDINGS Rhythm classification was the first application of AI-ECG. Subsequently, AI-ECG models have been developed for screening structural heart disease including hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, pulmonary hypertension, and left ventricular systolic dysfunction. Further, AI models can predict future events like development of systolic heart failure and atrial fibrillation. AI-ECG exhibits potential in acute cardiac events and non-cardiac applications, including acute pulmonary embolism, electrolyte abnormalities, monitoring drugs therapy, sleep apnea, and predicting all-cause mortality. Many AI models in the domain of cardiac monitors and smart watches have received Food and Drug Administration (FDA) clearance for rhythm classification, while others for identification of cardiac amyloidosis, pulmonary hypertension and left ventricular dysfunction have received breakthrough device designation. As AI-ECG models continue to be developed, in addition to regulatory oversight and monetization challenges, thoughtful clinical implementation to streamline workflows, avoiding information overload and overwhelming of healthcare systems with false positive results is necessary. Research to demonstrate and validate improvement in healthcare efficiency and improved patient outcomes would be required before widespread adoption of any AI-ECG model.
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Affiliation(s)
- Benjamin Ose
- The University of Kansas School of Medicine, Kansas City, KS, USA
| | - Zeeshan Sattar
- Division of General and Hospital Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amulya Gupta
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | | | - Chris Harvey
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA
| | - Amit Noheria
- Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, USA.
- Program for AI & Research in Cardiovascular Medicine (PARC), The University of Kansas Medical Center, Kansas City, KS, USA.
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Witharana P, Chang L, Maier R, Ogundimu E, Wilkinson C, Athanasiou T, Akowuah E. Feasibility study of rehabilitation for cardiac patients aided by an artificial intelligence web-based programme: a randomised controlled trial (RECAP trial)-a study protocol. BMJ Open 2024; 14:e079404. [PMID: 38688664 PMCID: PMC11086203 DOI: 10.1136/bmjopen-2023-079404] [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: 08/30/2023] [Accepted: 02/20/2024] [Indexed: 05/02/2024] Open
Abstract
INTRODUCTION Cardiac rehabilitation (CR) delivered by rehabilitation specialists in a healthcare setting is effective in improving functional capacity and reducing readmission rates after cardiac surgery. It is also associated with a reduction in cardiac mortality and recurrent myocardial infarction. This trial assesses the feasibility of a home-based CR programme delivered using a mobile application (app). METHODS The Rehabilitation through Exercise prescription for Cardiac patients using an Artificial intelligence web-based Programme (RECAP) randomised controlled feasibility trial is a single-centre prospective study, in which patients will be allocated on a 1:1 ratio to a home-based CR programme delivered using a mobile app with accelerometers or standard hospital-based rehabilitation classes. The home-based CR programme will employ artificial intelligence to prescribe exercise goals to the participants on a weekly basis. The trial will recruit 70 patients in total. The primary objectives are to evaluate participant recruitment and dropout rates, assess the feasibility of randomisation, determine acceptability to participants and staff, assess the rates of potential outcome measures and determine hospital resource allocation to inform the design of a larger randomised controlled trial for clinical efficacy and health economic evaluation. Secondary objectives include evaluation of health-related quality of life and 6 minute walk distance. ETHICS AND DISSEMINATION RECAP trial received a favourable outcome from the Berkshire research ethics committee in September 2022 (IRAS 315483).Trial results will be made available through publication in peer-reviewed journals and presented at relevant scientific meetings. TRIAL REGISTRATION NUMBER ISRCTN97352737.
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Affiliation(s)
- Pasan Witharana
- Academic Cardiovascular Unit, South Tees Hospitals NHS Foundation Trust, Middlesbrough, UK
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Lisa Chang
- Academic Cardiovascular Unit, South Tees Hospitals NHS Foundation Trust, Middlesbrough, UK
| | - Rebecca Maier
- Academic Cardiovascular Unit, South Tees Hospitals NHS Foundation Trust, Middlesbrough, UK
| | | | - Christopher Wilkinson
- Academic Cardiovascular Unit, South Tees Hospitals NHS Foundation Trust, Middlesbrough, UK
- Hull York Medical School, University of York, York, UK
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Enoch Akowuah
- Academic Cardiovascular Unit, South Tees Hospitals NHS Foundation Trust, Middlesbrough, UK
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Almansouri NE, Awe M, Rajavelu S, Jahnavi K, Shastry R, Hasan A, Hasan H, Lakkimsetti M, AlAbbasi RK, Gutiérrez BC, Haider A. Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review. Cureus 2024; 16:e55869. [PMID: 38595869 PMCID: PMC11002715 DOI: 10.7759/cureus.55869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/09/2024] [Indexed: 04/11/2024] Open
Abstract
Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.
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Affiliation(s)
| | - Mishael Awe
- Internal Medicine, Crimea State Medical University named after S.I Georgievsky, Simferopol, UKR
| | - Selvambigay Rajavelu
- Internal Medicine, Sri Ramachandra Institute of Higher Education and Research, Chennai, IND
| | - Kudapa Jahnavi
- Internal Medicine, Pondicherry Institute of Medical Sciences, Puducherry, IND
| | - Rohan Shastry
- Internal Medicine, Vydehi Institute of Medical Sciences and Research Center, Bengaluru, IND
| | - Ali Hasan
- Internal Medicine, University of Illinois at Chicago, Chicago, USA
| | - Hadi Hasan
- Internal Medicine, University of Illinois, Chicago, USA
| | | | | | - Brian Criollo Gutiérrez
- Health Sciences, Instituto Colombiano de Estudios Superiores de Incolda (ICESI) University, Cali, COL
| | - Ali Haider
- Allied Health Sciences, The University of Lahore, Gujrat, PAK
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Zhou R, Li Z, Liu J, Qian D, Meng X, Guan L, Sun X, Li H, Yu M. Prediction of intraoperative red blood cell transfusion in valve replacement surgery: machine learning algorithm development based on non-anemic cohort. Front Cardiovasc Med 2024; 11:1344170. [PMID: 38486703 PMCID: PMC10937389 DOI: 10.3389/fcvm.2024.1344170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 02/20/2024] [Indexed: 03/17/2024] Open
Abstract
Background Our study aimed to develop machine learning algorithms capable of predicting red blood cell (RBC) transfusion during valve replacement surgery based on a preoperative dataset of the non-anemic cohort. Methods A total of 423 patients who underwent valvular replacement surgery from January 2015 to December 2020 were enrolled. A comprehensive database that incorporated demographic characteristics, clinical conditions, and results of preoperative biochemistry tests was used for establishing the models. A range of machine learning algorithms were employed, including decision tree, random forest, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), support vector classifier and logistic regression (LR). Subsequently, the area under the receiver operating characteristic curve (AUC), accuracy, recall, precision, and F1 score were used to determine the predictive capability of the algorithms. Furthermore, we utilized SHapley Additive exPlanation (SHAP) values to explain the optimal prediction model. Results The enrolled patients were randomly divided into training set and testing set according to the 8:2 ratio. There were 16 important features identified by Sequential Backward Selection for model establishment. The top 5 most influential features in the RF importance matrix plot were hematocrit, hemoglobin, ALT, fibrinogen, and ferritin. The optimal prediction model was CatBoost algorithm, exhibiting the highest AUC (0.752, 95% CI: 0.662-0.780), which also got relatively high F1 score (0.695). The CatBoost algorithm also showed superior performance over the LR model with the AUC (0.666, 95% CI: 0.534-0.697). The SHAP summary plot and the SHAP dependence plot were used to visually illustrate the positive or negative effects of the selected features attributed to the CatBoost model. Conclusions This study established a series of prediction models to enhance risk assessment of intraoperative RBC transfusion during valve replacement in no-anemic patients. The identified important predictors may provide effective preoperative interventions.
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Affiliation(s)
- Ren Zhou
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Shanghai Institute of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhaolong Li
- Department of Cardiovascular Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Liu
- Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dewei Qian
- Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiangdong Meng
- Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lichun Guan
- Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinxin Sun
- Department of Cardiovascular Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Haiqing Li
- Department of Cardiovascular Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min Yu
- Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Chan PZ, Ramli MAIB, Chew HSJ. Diagnostic Test Accuracy of artificial intelligence-assisted detection of acute coronary syndrome: A systematic review and meta-analysis. Comput Biol Med 2023; 167:107636. [PMID: 37925910 DOI: 10.1016/j.compbiomed.2023.107636] [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: 07/23/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Artificial intelligence (AI) has potential uses in healthcare including the detection of health conditions and prediction of health outcomes. Past systematic reviews had reviewed the accuracy of artificial neural networks (ANN) on Electrocardiogram (ECG) readings but that of other AI models on other Acute Coronary Syndrome (ACS) detection tools remains unclear. METHODS Nine electronic databases were searched from 2012 to 31 August 2022 including grey literature search and hand searching of references of included articles. Risk of bias was assessed by two independent reviewers using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Test characteristics namely true positives, false positives, true negatives, and false negatives were extracted from all included articles into a 2x2 table. Study-specific estimates of sensitivity and specificity were pooled using hierarchical summary receiver operating characteristic (HSROC) model and displayed using a forest plot and HSROC curve. RESULTS 66 studies were included in the review. A total of 518,931 patients were included whose mean ages varied from 32.62 to 70 years old. In 66 studies, the sensitivity and specificity of AI-based detection for ACS screening ranged from 64 % to 100 % and 65 %-100 %, respectively. The overall quality of evidence was low due to the inclusion of case-control studies. CONCLUSION Results of the study inform the potential of using AI-assisted ACS detection for accurate diagnosis and prompt treatment for ACS. Adherence to the Standards for Reporting of Diagnostic Accuracy (STARD) guideline and having more cohort studies for future Diagnostic Test Accuracy (DTA) studies are necessary to improve the quality of evidence of AI-based detection of ACS.
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Affiliation(s)
- Pin Zhong Chan
- Alice Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore
| | - Muhammad Aqil Irfan Bin Ramli
- Alice Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore
| | - Han Shi Jocelyn Chew
- Alice Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore.
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Letelier P, Saldías R, Loren P, Riquelme I, Guzmán N. MicroRNAs as Potential Biomarkers of Environmental Exposure to Polycyclic Aromatic Hydrocarbons and Their Link with Inflammation and Lung Cancer. Int J Mol Sci 2023; 24:16984. [PMID: 38069307 PMCID: PMC10707120 DOI: 10.3390/ijms242316984] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/26/2023] [Accepted: 10/28/2023] [Indexed: 12/18/2023] Open
Abstract
Exposure to atmospheric air pollution containing volatile organic compounds such as polycyclic aromatic hydrocarbons (PAHs) has been shown to be a risk factor in the induction of lung inflammation and the initiation and progression of lung cancer. MicroRNAs (miRNAs) are small single-stranded non-coding RNA molecules of ~20-22 nucleotides that regulate different physiological processes, and their altered expression is implicated in various pathophysiological conditions. Recent studies have shown that the regulation of gene expression of miRNAs can be affected in diseases associated with outdoor air pollution, meaning they could also be useful as biomarkers of exposure to environmental pollution. In this article, we review the published evidence on miRNAs in relation to exposure to PAH pollution and discuss the possible mechanisms that may link these compounds with the expression of miRNAs.
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Affiliation(s)
- Pablo Letelier
- Laboratorio de Investigación en Salud de Precisión, Departamento de Procesos Diagnósticos y Evaluación, Facultad de Ciencias de la Salud, Universidad Católica de Temuco, Temuco 4813302, Chile; (R.S.); (N.G.)
| | - Rolando Saldías
- Laboratorio de Investigación en Salud de Precisión, Departamento de Procesos Diagnósticos y Evaluación, Facultad de Ciencias de la Salud, Universidad Católica de Temuco, Temuco 4813302, Chile; (R.S.); (N.G.)
| | - Pía Loren
- Center of Molecular Biology and Pharmacogenetics, Scientific and Technological Bioresource Nucleus, Universidad de La Frontera, Temuco 4811230, Chile;
| | - Ismael Riquelme
- Institute of Biomedical Sciences, Faculty of Health Sciences, Universidad Autónoma de Chile, Temuco 4810101, Chile;
| | - Neftalí Guzmán
- Laboratorio de Investigación en Salud de Precisión, Departamento de Procesos Diagnósticos y Evaluación, Facultad de Ciencias de la Salud, Universidad Católica de Temuco, Temuco 4813302, Chile; (R.S.); (N.G.)
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Zareiamand H, Darroudi A, Mohammadi I, Moravvej SV, Danaei S, Alizadehsani R. Cardiac Magnetic Resonance Imaging (CMRI) Applications in Patients with Chest Pain in the Emergency Department: A Narrative Review. Diagnostics (Basel) 2023; 13:2667. [PMID: 37627926 PMCID: PMC10453831 DOI: 10.3390/diagnostics13162667] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
CMRI is the exclusive imaging technique capable of identifying myocardial edema, endomyocardial fibrosis, pericarditis accompanied by pericardial effusions, and apical thrombi within either the left or right ventricle. In this work, we examine the research literature on the use of CMRI in the diagnosis of chest discomfort, employing randomized controlled trials (RCTs) to evaluate its effectiveness. The research outlines the disorders of the chest and the machine learning approaches for detecting them. In conclusion, the study ends with an examination of a fundamental illustration of CMRI analysis. To find a comprehensive review, the Scopus scientific resource is analyzed. The issue, based on the findings, is to distinguish ischemia from non-ischemic cardiac causes of chest pain in individuals presenting with sudden chest pain or discomfort upon arrival at the emergency department (ED). Due to the failure of conventional methods in accurately diagnosing acute cardiac ischemia, individuals are still being inappropriately discharged from the ED, resulting in a heightened death rate.
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Affiliation(s)
- Hossein Zareiamand
- Department of Cardiology, Faculty of Medicine, Sari Branch, Islamic Azad University, Sari 48161-19318, Iran;
| | - Amin Darroudi
- Student Research Committee, Sari Branch, Islamic Azad University, Sari 48161-19318, Iran;
| | - Iraj Mohammadi
- Department of Basic Sciences, Faculty of Medicine, Sari Branch, Islamic Azad University, Sari 48161-19318, Iran;
| | - Seyed Vahid Moravvej
- Department of Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran;
| | - Saba Danaei
- Adiban Institute of Higher Education, Garmsar 35881-43112, Iran;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
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Yin Z, Yao C, Zhang L, Qi S. Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect. Front Med (Lausanne) 2023; 10:1128084. [PMID: 36968824 PMCID: PMC10030915 DOI: 10.3389/fmed.2023.1128084] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/13/2023] [Indexed: 03/29/2023] Open
Abstract
In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.
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Affiliation(s)
- Zugang Yin
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chenhui Yao
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Limin Zhang
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shaohua Qi
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
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12
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Development and Validation of a Machine Learning Predictive Model for Cardiac Surgery-Associated Acute Kidney Injury. J Clin Med 2023; 12:jcm12031166. [PMID: 36769813 PMCID: PMC9917969 DOI: 10.3390/jcm12031166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 01/16/2023] [Accepted: 01/27/2023] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE We aimed to develop and validate a predictive machine learning (ML) model for cardiac surgery associated with acute kidney injury (CSA-AKI) based on a multicenter randomized control trial (RCT) and a Medical Information Mart for Intensive Care-IV (MIMIC-IV) dataset. METHODS This was a subanalysis from a completed RCT approved by the Ethics Committee of Fuwai Hospital in Beijing, China (NCT03782350). Data from Fuwai Hospital were randomly assigned, with 80% for the training dataset and 20% for the testing dataset. The data from three other centers were used for the external validation dataset. Furthermore, the MIMIC-IV dataset was also utilized to validate the performance of the predictive model. The area under the receiver operating characteristic curve (ROC-AUC), the precision-recall curve (PR-AUC), and the calibration brier score were applied to evaluate the performance of the traditional logistic regression (LR) and eleven ML algorithms. Additionally, the Shapley Additive Explanations (SHAP) interpreter was used to explain the potential risk factors for CSA-AKI. RESULT A total of 6495 eligible patients undergoing cardiopulmonary bypass (CPB) were eventually included in this study, 2416 of whom were from Fuwai Hospital (Beijing), for model development, 562 from three other cardiac centers in China, and 3517 from the MIMICIV dataset, were used, respectively, for external validation. The CatBoostClassifier algorithms outperformed other models, with excellent discrimination and calibration performance for the development, as well as the MIMIC-IV, datasets. In addition, the CatBoostClassifier achieved ROC-AUCs of 0.85, 0.67, and 0.77 and brier scores of 0.14, 0.19, and 0.16 in the testing, external, and MIMIC-IV datasets, respectively. Moreover, the utmost important risk factor, the N-terminal brain sodium peptide (NT-proBNP), was confirmed by the LASSO method in the feature section process. Notably, the SHAP explainer identified that the preoperative blood urea nitrogen level, prothrombin time, serum creatinine level, total bilirubin level, and age were positively correlated with CSA-AKI; preoperative platelets level, systolic and diastolic blood pressure, albumin level, and body weight were negatively associated with CSA-AKI. CONCLUSIONS The CatBoostClassifier algorithms outperformed other ML models in the discrimination and calibration of CSA-AKI prediction cardiac surgery with CPB, based on a multicenter RCT and MIMIC-IV dataset. Moreover, the preoperative NT-proBNP level was confirmed to be strongly related to CSA-AKI.
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13
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Lin W, Jia S, Chen Y, Shi H, Zhao J, Li Z, Wu Y, Jiang H, Zhang Q, Wang W, Chen Y, Feng C, Xia S. Korotkoff sounds dynamically reflect changes in cardiac function based on deep learning methods. Front Cardiovasc Med 2022; 9:940615. [PMID: 36093170 PMCID: PMC9458936 DOI: 10.3389/fcvm.2022.940615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/18/2022] [Indexed: 11/24/2022] Open
Abstract
Korotkoff sounds (K-sounds) have been around for over 100 years and are considered the gold standard for blood pressure (BP) measurement. K-sounds are also unique for the diagnosis and treatment of cardiovascular diseases; however, their efficacy is limited. The incidences of heart failure (HF) are increasing, which necessitate the development of a rapid and convenient pre-hospital screening method. In this review, we propose a deep learning (DL) method and the possibility of using K-methods to predict cardiac function changes for the detection of cardiac dysfunctions.
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Affiliation(s)
- Wenting Lin
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Sixiang Jia
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Yiwen Chen
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Hanning Shi
- Department of Anime and Comics, Hangzhou Normal University, Hangzhou, China
| | - Jianqiang Zhao
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Zhe Li
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Yiteng Wu
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Hangpan Jiang
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Qi Zhang
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Wei Wang
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Yayu Chen
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Chao Feng
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Shudong Xia
- Department of Cardiology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
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Lu H, Yao Y, Wang L, Yan J, Tu S, Xie Y, He W. Research Progress of Machine Learning and Deep Learning in Intelligent Diagnosis of the Coronary Atherosclerotic Heart Disease. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3016532. [PMID: 35516452 PMCID: PMC9064517 DOI: 10.1155/2022/3016532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 02/27/2022] [Accepted: 03/04/2022] [Indexed: 11/17/2022]
Abstract
The coronary atherosclerotic heart disease is a common cardiovascular disease with high morbidity, disability, and societal burden. Early, precise, and comprehensive diagnosis of the coronary atherosclerotic heart disease is of great significance. The rise of artificial intelligence technologies, represented by machine learning and deep learning, provides new methods to address the above issues. In recent years, artificial intelligence has achieved an extraordinary progress in multiple aspects of coronary atherosclerotic heart disease diagnosis, including the construction of intelligent diagnostic models based on artificial intelligence algorithms, applications of artificial intelligence algorithms in coronary angiography, coronary CT angiography, intravascular imaging, cardiac magnetic resonance, and functional parameters. This paper presents a comprehensive review of the technical background and current state of research on the application of artificial intelligence in the diagnosis of the coronary atherosclerotic heart disease and analyzes recent challenges and perspectives in this field.
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Affiliation(s)
- Haoxuan Lu
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
| | - Yudong Yao
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
| | - Li Wang
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
| | - Jianing Yan
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
| | - Shuangshuang Tu
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
| | - Yanqing Xie
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
| | - Wenming He
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China
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15
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Current and Future Applications of Artificial Intelligence in Coronary Artery Disease. Healthcare (Basel) 2022; 10:healthcare10020232. [PMID: 35206847 PMCID: PMC8872080 DOI: 10.3390/healthcare10020232] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/19/2022] [Accepted: 01/24/2022] [Indexed: 02/07/2023] Open
Abstract
Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.
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Infante T, Cavaliere C, Punzo B, Grimaldi V, Salvatore M, Napoli C. Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review. Circ Cardiovasc Imaging 2021; 14:1133-1146. [PMID: 34915726 DOI: 10.1161/circimaging.121.013025] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The risk of coronary heart disease (CHD) clinical manifestations and patient management is estimated according to risk scores accounting multifactorial risk factors, thus failing to cover the individual cardiovascular risk. Technological improvements in the field of medical imaging, in particular, in cardiac computed tomography angiography and cardiac magnetic resonance protocols, laid the development of radiogenomics. Radiogenomics aims to integrate a huge number of imaging features and molecular profiles to identify optimal radiomic/biomarker signatures. In addition, supervised and unsupervised artificial intelligence algorithms have the potential to combine different layers of data (imaging parameters and features, clinical variables and biomarkers) and elaborate complex and specific CHD risk models allowing more accurate diagnosis and reliable prognosis prediction. Literature from the past 5 years was systematically collected from PubMed and Scopus databases, and 60 studies were selected. We speculated the applicability of radiogenomics and artificial intelligence through the application of machine learning algorithms to identify CHD and characterize atherosclerotic lesions and myocardial abnormalities. Radiomic features extracted by cardiac computed tomography angiography and cardiac magnetic resonance showed good diagnostic accuracy for the identification of coronary plaques and myocardium structure; on the other hand, few studies exploited radiogenomics integration, thus suggesting further research efforts in this field. Cardiac computed tomography angiography resulted the most used noninvasive imaging modality for artificial intelligence applications. Several studies provided high performance for CHD diagnosis, classification, and prognostic assessment even though several efforts are still needed to validate and standardize algorithms for CHD patient routine according to good medical practice.
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Affiliation(s)
- Teresa Infante
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy (T.I., C.N.)
| | | | - Bruna Punzo
- IRCCS SDN, Naples, Italy (C.C., B.P., V.G., M.S., C.N.)
| | | | | | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy (T.I., C.N.).,IRCCS SDN, Naples, Italy (C.C., B.P., V.G., M.S., C.N.)
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17
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Stevens BR, Pepine CJ. Emerging role of machine learning in cardiovascular disease investigation and translations. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2021; 11:100050. [PMID: 38559318 PMCID: PMC10978128 DOI: 10.1016/j.ahjo.2021.100050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/02/2021] [Accepted: 09/01/2021] [Indexed: 04/04/2024]
Abstract
Unexpected insights and practical advances in cardiovascular disease (CVD) are being discovered by rapidly advancing developments in supercomputers and machine learning (ML) software algorithms. These have been accelerated during the COVID-19 pandemic, and the resulting CVD translational implications of ML are steering new measures of prevention and treatment, new tools for objective clinical diagnosis, and even opportunities for rethinking basic foundations of CVD nosology. As the usual cardiovascular specialist may not be familiar with these tools, the editor has invited this brief overview.
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Affiliation(s)
- Bruce R. Stevens
- Department of Physiology and Functional Genomics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Carl J. Pepine
- Division of Cardiovascular Medicine, University of Florida College of Medicine, Gainesville, FL, USA
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