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Fassina L, Muzio FPL, Berboth L, Ötvös J, Faragli A, Alogna A. Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study. J Cardiovasc Transl Res 2024:10.1007/s12265-024-10546-2. [PMID: 39017912 DOI: 10.1007/s12265-024-10546-2] [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: 11/02/2023] [Accepted: 07/10/2024] [Indexed: 07/18/2024]
Abstract
Heart failure (HF) is defined as the inability of the heart to meet body oxygen demand requiring an elevation in left ventricular filling pressures (LVP) to compensate. LVP increase can be assessed in the cardiac catheterization laboratory, but this procedure is invasive and time-consuming to the extent that physicians rather rely on non-invasive diagnostic tools. In this work, we assess the feasibility to develop a novel machine-learning (ML) approach to predict clinically relevant LVP indices. Synchronized invasive (pressure-volume tracings) and non-invasive signals (ECG, pulse oximetry, and cardiac sounds) were collected from anesthetized, closed-chest Göttingen minipigs. Animals were either healthy or had HF with reduced ejection fraction and circa 500 heartbeats were included in the analysis for each animal. The ML algorithm showed excellent prediction of LVP indices estimating, for instance, the end-diastolic pressure with a R2 of 0.955. This novel ML algorithm could assist clinicians in the care of HF patients.
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Affiliation(s)
- Lorenzo Fassina
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, Pavia, 27100, Italy.
| | - Francesco Paolo Lo Muzio
- Department of Cardiology, Deutsches Herzzentrum Der Charité, Angiology and Intensive Care Medicine, Campus Virchow-Klinikum, Augustenburgerplatz 1, Berlin, 13353, Germany
| | - Leonhard Berboth
- Department of Cardiology, Deutsches Herzzentrum Der Charité, Angiology and Intensive Care Medicine, Campus Virchow-Klinikum, Augustenburgerplatz 1, Berlin, 13353, Germany
| | - Jens Ötvös
- Department of Cardiology, Deutsches Herzzentrum Der Charité, Angiology and Intensive Care Medicine, Campus Virchow-Klinikum, Augustenburgerplatz 1, Berlin, 13353, Germany
| | - Alessandro Faragli
- Department of Cardiology, Deutsches Herzzentrum Der Charité, Angiology and Intensive Care Medicine, Campus Virchow-Klinikum, Augustenburgerplatz 1, Berlin, 13353, Germany
| | - Alessio Alogna
- Department of Cardiology, Deutsches Herzzentrum Der Charité, Angiology and Intensive Care Medicine, Campus Virchow-Klinikum, Augustenburgerplatz 1, Berlin, 13353, Germany.
- DZHK (German Centre for Cardiovascular Research), Partner Site, Berlin, 10785, Germany.
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2
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Oikonomou EK, Khera R. Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility. Hellenic J Cardiol 2024:S1109-9666(24)00158-1. [PMID: 39025234 DOI: 10.1016/j.hjc.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/21/2024] [Accepted: 07/02/2024] [Indexed: 07/20/2024] Open
Abstract
Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient, and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remain unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance, and the importance of vendor-agnostic data types that will be available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology.
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Affiliation(s)
- Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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3
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Islam MS, Kalmady SV, Hindle A, Sandhu R, Sun W, Sepehrvand N, Greiner R, Kaul P. Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications. Can J Cardiol 2024:S0828-282X(24)00523-3. [PMID: 38992812 DOI: 10.1016/j.cjca.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/13/2024] Open
Abstract
Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, noncardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases in the past 5 years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, most of these studies are single-centre, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for < 15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration have been developed through commercial collaborations, with approximately half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multicentre large data sets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in cardiovascular disease management.
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Affiliation(s)
- Md Saiful Islam
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Sunil Vasu Kalmady
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Abram Hindle
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Roopinder Sandhu
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Smidt Heart Institute, Cedars-Sinai Medical Center Hospital System, Los Angeles, California, USA
| | - Weijie Sun
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Nariman Sepehrvand
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada
| | - Padma Kaul
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
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4
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Khera R, Oikonomou EK, Nadkarni GN, Morley JR, Wiens J, Butte AJ, Topol EJ. Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review. J Am Coll Cardiol 2024; 84:97-114. [PMID: 38925729 DOI: 10.1016/j.jacc.2024.05.003] [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: 04/29/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 06/28/2024]
Abstract
Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all.
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Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA; Center for Outcomes Research and Evaluation (CORE), New Haven, Connecticut, USA; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Girish N Nadkarni
- The Samuel Bronfman Department of Medicine, Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jessica R Morley
- Digital Ethics Center, Yale University, New Haven, Connecticut, USA
| | - Jenna Wiens
- Electrical Engineering and Computer Science, Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA; Center for Data-Driven Insights and Innovation, University of California Health, Oakland, California, USA
| | - Eric J Topol
- Molecular Medicine, Scripps Research Translational Institute, Scripps Research, La Jolla, California, USA
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Reavette RM, Ramakrishnan A, Rowland EM, Tang MX, Mayet J, Weinberg PD. Detecting heart failure from B-mode ultrasound characterization of arterial pulse waves. Am J Physiol Heart Circ Physiol 2024; 327:H80-H88. [PMID: 38787379 DOI: 10.1152/ajpheart.00219.2024] [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: 04/09/2024] [Revised: 04/29/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024]
Abstract
This study investigated the sensitivity and specificity of identifying heart failure with reduced ejection fraction (HFrEF) from measurements of the intensity and timing of arterial pulse waves. Previously validated methods combining ultrafast B-mode ultrasound, plane-wave transmission, singular value decomposition (SVD), and speckle tracking were used to characterize the compression and decompression ("S" and "D") waves occurring in early and late systole, respectively, in the carotid arteries of outpatients with left ventricular ejection fraction (LVEF) < 40%, determined by echocardiography, and signs and symptoms of heart failure, or with LVEF ≥ 50% and no signs or symptoms of heart failure. On average, the HFrEF group had significantly reduced S-wave intensity and energy, a greater interval between the R wave of the ECG and the S wave, a reduced interval between the S and D waves, and an increase in the S-wave shift (SWS), a novel metric that characterizes the shift in timing of the S wave away from the R wave of the ECG and toward the D wave (all P < 0.01). Receiver operating characteristics (ROCs) were used to quantify for the first time how well wave metrics classified individual participants. S-wave intensity and energy gave areas under the ROC of 0.76-0.83, the ECG-S-wave interval gave 0.85-0.88, and the S-wave shift gave 0.88-0.92. Hence the methods, which are simple to use and do not require complex interpretation, provide sensitive and specific identification of HFrEF. If similar results were obtained in primary care, they could form the basis of techniques for heart failure screening.NEW & NOTEWORTHY We show that heart failure with reduced ejection fraction can be detected with excellent sensitivity and specificity in individual patients by using B-mode ultrasound to detect altered pulse wave intensity and timing in the carotid artery.
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Affiliation(s)
- Ryan M Reavette
- Department of Bioengineering, Imperial College, London, United Kingdom
| | - Anenta Ramakrishnan
- Department of Bioengineering, Imperial College, London, United Kingdom
- Department of Cardiology, The Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Ethan M Rowland
- Department of Bioengineering, Imperial College, London, United Kingdom
| | - Meng-Xing Tang
- Department of Bioengineering, Imperial College, London, United Kingdom
| | - Jamil Mayet
- Department of Cardiology, The Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Peter D Weinberg
- Department of Bioengineering, Imperial College, London, United Kingdom
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6
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Howard JP, Vasudevan N, Sarkar S, Landman S, Koehler J, Keene D. Dynamic risk stratification of worsening heart failure using a deep learning-enabled implanted ambulatory single-lead electrocardiogram. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:435-443. [PMID: 39081943 PMCID: PMC11284004 DOI: 10.1093/ehjdh/ztae035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 01/27/2024] [Accepted: 02/21/2024] [Indexed: 08/02/2024]
Abstract
Aims Implantable loop recorders (ILRs) provide continuous single-lead ambulatory electrocardiogram (aECG) monitoring. Whether these aECGs could be used to identify worsening heart failure (HF) is unknown. Methods and results We linked ILR aECG from Medtronic device database to the left ventricular ejection fraction (LVEF) measurements in Optum® de-identified electronic health record dataset. We trained an artificial intelligence (AI) algorithm [aECG-convolutional neural network (CNN)] on a dataset of 35 741 aECGs from 2247 patients to identify LVEF ≤ 40% and assessed its performance using the area under the receiver operating characteristic curve. Ambulatory electrocardiogram-CNN was then used to identify patients with increasing risk of HF hospitalization in a real-world cohort of 909 patients with prior HF diagnosis. This dataset provided 12 467 follow-up monthly evaluations, with 201 HF hospitalizations. For every month, time-series features from these predictions were used to categorize patients into high- and low-risk groups and predict HF hospitalization in the next month. The risk of HF hospitalization in the next 30 days was significantly higher in the cohort that aECG-CNN identified as high risk [hazard ratio (HR) 1.89; 95% confidence interval (CI) 1.28-2.79; P = 0.001] compared with low risk, even after adjusting patient demographics (HR 1.88; 95% CI 1.27-2.79 P = 0.002). Conclusion An AI algorithm trained to detect LVEF ≤40% using ILR aECGs can also readily identify patients at increased risk of HF hospitalizations by monitoring changes in the probability of HF over 30 days.
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Affiliation(s)
- James Philip Howard
- National Heart and Lung Institute, Imperial College London, Du Cane Road, W12 0HS, London, UK
| | - Neethu Vasudevan
- Research and Technology, Cardiac Rhythm Management, Medtronic Inc., Minneapolis, MN, USA
| | - Shantanu Sarkar
- Research and Technology, Cardiac Rhythm Management, Medtronic Inc., Minneapolis, MN, USA
| | - Sean Landman
- Research and Technology, Cardiac Rhythm Management, Medtronic Inc., Minneapolis, MN, USA
| | - Jodi Koehler
- Research and Technology, Cardiac Rhythm Management, Medtronic Inc., Minneapolis, MN, USA
| | - Daniel Keene
- National Heart and Lung Institute, Imperial College London, Du Cane Road, W12 0HS, London, UK
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7
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Conti DM, Vibeke B, Kirsten B, Leif B, Adam C, Stephanie D, Mina G, Monika G, Philippe G, Eckard H, Hellings PW, Milos J, Kopp MV, Marcus M, Marcia P, Dermot R, Scadding GK, Eike W, Ulrich W, Susanne L. EUFOREUM Berlin 2023: Optimizing care for type 2 inflammatory diseases from clinic to AI: A pediatric focus. Pediatr Allergy Immunol 2024; 35:e14183. [PMID: 38949196 DOI: 10.1111/pai.14183] [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: 02/16/2024] [Revised: 06/04/2024] [Accepted: 06/12/2024] [Indexed: 07/02/2024]
Abstract
The European Forum for Research and Education in Allergy and Airways diseases (EUFOREA) organized its bi-annual forum EUFOREUM in Berlin in November 2023. The aim of EUFOREUM 2023 was to highlight pediatric action plans for prevention and optimizing care for type 2 inflammatory conditions starting in childhood, with a focus on early-stage diagnosis, ensuring neither under- nor overdiagnosis, optimal care, and suggestions for improvement of care. EUFOREA is an international not-for-profit organization forming an alliance of all stakeholders dedicated to reducing the prevalence and burden of chronic respiratory diseases through the implementation of optimal patient care via educational, research, and advocacy activities. The inclusive and multidisciplinary approach of EUFOREA was reflected in the keynote lectures and faculty of the virtual EUFOREUM 2023 (www.euforea.eu/euforeum) coming from the pediatric, allergology, pulmonology, ENT, dermatology, primary health care fields and patients around the central theme of type 2 inflammation. As most type 2 inflammatory conditions may start in childhood or adolescence, and most children have type 2 inflammation when suffering from a respiratory or skin disease, the moment has come to raise the bar of ambitions of care, including prevention, remission and disease modification at an early stage. The current report provides a comprehensive overview of key statements by the faculty of the EUFOREUM 2023 and the ambitions of EUFOREA allowing all stakeholders in the respiratory field to be updated and ready to join forces in Europe and beyond.
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Affiliation(s)
- Diego M Conti
- The European Forum for Research and Education in Allergy and Airway Diseases Scientific Expert Team Members, Brussels, Belgium
- Escuela de Doctorado UAM, Centro de Estudios de Posgrado, Universidad Autónoma de Madrid, Madrid, Spain
| | - Backer Vibeke
- Department of Otorhinolaryngology, Head & Neck Surgery, and Audiology, Rigshospitalet, Copenhagen University, Copenhagen, Denmark
| | - Beyer Kirsten
- Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Bjermer Leif
- Department of Respiratory Medicine & Allergology, Institute for Clinical Science, Skane University Hospital, Lund University, Lund, Sweden
| | - Chaker Adam
- Department of Otorhinolaryngology and Center for Allergy and Environment (ZAUM), TUM School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Dramburg Stephanie
- Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Gaga Mina
- 1st Respiratory Medicine Department, Hygeia Hospital, Marousi, Greece
- WHO Europe, Standing Committee SCRC
| | - Gappa Monika
- Department of Pediatrics, Evangelisches Krankenhaus Düsseldorf, Düsseldorf, Germany
| | - Gevaert Philippe
- Laboratory of Upper Airways Research, Department of Otorhinolaryngology, University of Ghent, Ghent, Belgium
| | - Hamelmann Eckard
- Children's Center Bethel, University Hospital Bielefeld, University Bielefeld, Bielefeld, Germany
| | - Peter W Hellings
- Laboratory of Upper Airways Research, Department of Otorhinolaryngology, University of Ghent, Ghent, Belgium
- KU Leuven Department of Microbiology and Immunology, Allergy and Clinical Immunology Research Unit, Leuven, Belgium
- Clinical Department of Otorhinolaryngology, Head and Neck Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Jesenak Milos
- Department of Pulmonology and Phthisiology, Department of Pediatrics, Department of Clinical Immunology and Allergology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, University Hospital in Martin, Martin, Slovakia
| | - Matthias V Kopp
- Division of Paediatric Pneumology and Allergology, University Children's Hospital, University Medical Center Schleswig-Holstein Campus Luebeck, Luebeck, Germany
- Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Grosshansdorf, Germany
- Division of Paediatric Respiratory Medicine and Allergology, Department of Paediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Maurer Marcus
- Institute of Allergology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Immunology and Allergology, Berlin, Germany
| | - Podesta Marcia
- EFA - European Federation of Allergy and Airways Diseases Patients' Associations, Brussels, Belgium
| | - Ryan Dermot
- Allergy and Respiratory Research Group, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
- International Primary Care Respiratory Group, Edinburgh, UK
| | - Glenis K Scadding
- Department of Allergy & Rhinology, Royal National ENT Hospital, London, UK
- Division of Immunity and Infection, University College, London, UK
| | - Wüstenberg Eike
- Department of Otorhinolaryngology Head and Neck Surgery, Faculty of Medicine (and University Hospital) Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Wahn Ulrich
- Emeritus Department of Pediatric Pneumology and Immunology, Charité Universitaetsmedizin Berlin, Berlin, Germany
| | - Lau Susanne
- Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
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Huecker M, Schutzman C, French J, El-Kersh K, Ghafghazi S, Desai R, Frick D, Thomas JJ. Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study. JMIR Cardio 2024; 8:e57111. [PMID: 38924781 PMCID: PMC11237790 DOI: 10.2196/57111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 03/19/2024] [Accepted: 04/10/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Heart failure (HF) contributes greatly to morbidity, mortality, and health care costs worldwide. Hospital readmission rates are tracked closely and determine federal reimbursement dollars. No current modality or technology allows for accurate measurement of relevant HF parameters in ambulatory, rural, or underserved settings. This limits the use of telehealth to diagnose or monitor HF in ambulatory patients. OBJECTIVE This study describes a novel HF diagnostic technology using audio recordings from a standard mobile phone. METHODS This prospective study of acoustic microphone recordings enrolled convenience samples of patients from 2 different clinical sites in 2 separate areas of the United States. Recordings were obtained at the aortic (second intercostal) site with the patient sitting upright. The team used recordings to create predictive algorithms using physics-based (not neural networks) models. The analysis matched mobile phone acoustic data to ejection fraction (EF) and stroke volume (SV) as evaluated by echocardiograms. Using the physics-based approach to determine features eliminates the need for neural networks and overfitting strategies entirely, potentially offering advantages in data efficiency, model stability, regulatory visibility, and physical insightfulness. RESULTS Recordings were obtained from 113 participants. No recordings were excluded due to background noise or for any other reason. Participants had diverse racial backgrounds and body surface areas. Reliable echocardiogram data were available for EF from 113 patients and for SV from 65 patients. The mean age of the EF cohort was 66.3 (SD 13.3) years, with female patients comprising 38.3% (43/113) of the group. Using an EF cutoff of ≤40% versus >40%, the model (using 4 features) had an area under the receiver operating curve (AUROC) of 0.955, sensitivity of 0.952, specificity of 0.958, and accuracy of 0.956. The mean age of the SV cohort was 65.5 (SD 12.7) years, with female patients comprising 34% (38/65) of the group. Using a clinically relevant SV cutoff of <50 mL versus >50 mL, the model (using 3 features) had an AUROC of 0.922, sensitivity of 1.000, specificity of 0.844, and accuracy of 0.923. Acoustics frequencies associated with SV were observed to be higher than those associated with EF and, therefore, were less likely to pass through the tissue without distortion. CONCLUSIONS This work describes the use of mobile phone auscultation recordings obtained with unaltered cellular microphones. The analysis reproduced the estimates of EF and SV with impressive accuracy. This technology will be further developed into a mobile app that could bring screening and monitoring of HF to several clinical settings, such as home or telehealth, rural, remote, and underserved areas across the globe. This would bring high-quality diagnostic methods to patients with HF using equipment they already own and in situations where no other diagnostic and monitoring options exist.
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Affiliation(s)
- Martin Huecker
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
| | - Craig Schutzman
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
| | - Joshua French
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
| | - Karim El-Kersh
- Department of Pulmonary and Critical Care Medicine, The University of Arizona, Phoenix, AZ, United States
| | - Shahab Ghafghazi
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
| | - Ravi Desai
- Lehigh Valley Health Network Cardiology and Critical Care, Allentown, PA, United States
| | - Daniel Frick
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
| | - Jarred Jeremy Thomas
- Department of Emergency Medicine, University of Louisville, Louisville, KY, United States
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9
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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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10
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Alshahrani NS, Hartley A, Howard J, Hajhosseiny R, Khawaja S, Seligman H, Akbari T, Alharbi BA, Bassett P, Al-Lamee R, Francis D, Kaura A, Kelshiker MA, Peters NS, Khamis R. Randomized Trial of Remote Assessment of Patients After an Acute Coronary Syndrome. J Am Coll Cardiol 2024; 83:2250-2259. [PMID: 38588928 DOI: 10.1016/j.jacc.2024.03.398] [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: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/10/2024]
Abstract
BACKGROUND Telemedicine programs can provide remote diagnostic information to aid clinical decisions that could optimize care and reduce unplanned readmissions post-acute coronary syndrome (ACS). OBJECTIVES TELE-ACS (Remote Acute Assessment of Patients With High Cardiovascular Risk Post-Acute Coronary Syndrome) is a randomized controlled trial that aims to compare a telemedicine-based approach vs standard care in patients following ACS. METHODS Patients were suitable for inclusion with at least 1 cardiovascular risk factor and presenting with ACS and were randomized (1:1) before discharge. The primary outcome was time to first readmission at 6 months. Secondary outcomes included emergency department (ED) visits, major adverse cardiovascular events, and patient-reported symptoms. The primary analysis was performed according to intention to treat. RESULTS A total of 337 patients were randomized from January 2022 to April 2023, with a 3.6% drop-out rate. The mean age was 58.1 years. There was a reduced rate of readmission over 6 months (HR: 0.24; 95% CI: 0.13-0.44; P < 0.001) and ED attendance (HR: 0.59; 95% CI: 0.40-0.89) in the telemedicine arm, and fewer unplanned coronary revascularizations (3% in telemedicine arm vs 9% in standard therapy arm). The occurrence of chest pain (9% vs 24%), breathlessness (21% vs 39%), and dizziness (6% vs 18%) at 6 months was lower in the telemedicine group. CONCLUSIONS The TELE-ACS study has shown that a telemedicine-based approach for the management of patients following ACS was associated with a reduction in hospital readmission, ED visits, unplanned coronary revascularization, and patient-reported symptoms. (Telemedicine in High-Risk Cardiovascular Patients Post-ACS [TELE-ACS]; NCT05015634).
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Affiliation(s)
- Nasser S Alshahrani
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; King Khalid University, Abha, Saudi Arabia
| | - Adam Hartley
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, London, United Kingdom
| | - James Howard
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Reza Hajhosseiny
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Saud Khawaja
- Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Henry Seligman
- Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Tamim Akbari
- Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Badr A Alharbi
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; King Khalid University, Abha, Saudi Arabia
| | - Paul Bassett
- Statsconsultancy Ltd, Amersham, Buckinghamshire, United Kingdom
| | - Rasha Al-Lamee
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Darrel Francis
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Amit Kaura
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Mihir A Kelshiker
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Nicholas S Peters
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Ramzi Khamis
- National Heart and Lung Institute, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, London, United Kingdom.
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11
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Petmezas G, Papageorgiou VE, Vassilikos V, Pagourelias E, Tsaklidis G, Katsaggelos AK, Maglaveras N. Recent advancements and applications of deep learning in heart failure: Α systematic review. Comput Biol Med 2024; 176:108557. [PMID: 38728995 DOI: 10.1016/j.compbiomed.2024.108557] [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: 03/15/2024] [Revised: 04/12/2024] [Accepted: 05/05/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Heart failure (HF), a global health challenge, requires innovative diagnostic and management approaches. The rapid evolution of deep learning (DL) in healthcare necessitates a comprehensive review to evaluate these developments and their potential to enhance HF evaluation, aligning clinical practices with technological advancements. OBJECTIVE This review aims to systematically explore the contributions of DL technologies in the assessment of HF, focusing on their potential to improve diagnostic accuracy, personalize treatment strategies, and address the impact of comorbidities. METHODS A thorough literature search was conducted across four major electronic databases: PubMed, Scopus, Web of Science and IEEE Xplore, yielding 137 articles that were subsequently categorized into five primary application areas: cardiovascular disease (CVD) classification, HF detection, image analysis, risk assessment, and other clinical analyses. The selection criteria focused on studies utilizing DL algorithms for HF assessment, not limited to HF detection but extending to any attempt in analyzing and interpreting HF-related data. RESULTS The analysis revealed a notable emphasis on CVD classification and HF detection, with DL algorithms showing significant promise in distinguishing between affected individuals and healthy subjects. Furthermore, the review highlights DL's capacity to identify underlying cardiomyopathies and other comorbidities, underscoring its utility in refining diagnostic processes and tailoring treatment plans to individual patient needs. CONCLUSIONS This review establishes DL as a key innovation in HF management, highlighting its role in advancing diagnostic accuracy and personalized care. The insights provided advocate for the integration of DL in clinical settings and suggest directions for future research to enhance patient outcomes in HF care.
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Affiliation(s)
- Georgios Petmezas
- 2nd Department of Obstetrics and Gynecology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Centre for Research and Technology Hellas, Thessaloniki, Greece.
| | | | - Vasileios Vassilikos
- 3rd Department of Cardiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Efstathios Pagourelias
- 3rd Department of Cardiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - George Tsaklidis
- Department of Mathematics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
| | - Nicos Maglaveras
- 2nd Department of Obstetrics and Gynecology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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12
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Adedinsewo D, Morales-Lara AC, Hardway H, Johnson P, Young KA, Garzon-Siatoya WT, Butler Tobah YS, Rose CH, Burnette D, Seccombe K, Fussell M, Phillips S, Lopez-Jimenez F, Attia ZI, Friedman PA, Carter RE, Noseworthy PA. Artificial intelligence-based screening for cardiomyopathy in an obstetric population: A pilot study. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2024; 5:132-140. [PMID: 38989045 PMCID: PMC11232425 DOI: 10.1016/j.cvdhj.2024.03.005] [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] [Indexed: 07/12/2024] Open
Abstract
Background Cardiomyopathy is a leading cause of pregnancy-related mortality and the number one cause of death in the late postpartum period. Delay in diagnosis is associated with severe adverse outcomes. Objective To evaluate the performance of an artificial intelligence-enhanced electrocardiogram (AI-ECG) and AI-enabled digital stethoscope to detect left ventricular systolic dysfunction in an obstetric population. Methods We conducted a single-arm prospective study of pregnant and postpartum women enrolled at 3 sites between October 28, 2021, and October 27, 2022. Study participants completed a standard 12-lead ECG, digital stethoscope ECG and phonocardiogram recordings, and a transthoracic echocardiogram within 24 hours. Diagnostic performance was evaluated using the area under the curve (AUC). Results One hundred women were included in the final analysis. The median age was 31 years (Q1: 27, Q3: 34). Thirty-eight percent identified as non-Hispanic White, 32% as non-Hispanic Black, and 21% as Hispanic. Five percent and 6% had left ventricular ejection fraction (LVEF) <45% and <50%, respectively. The AI-ECG model had near-perfect classification performance (AUC: 1.0, 100% sensitivity; 99%-100% specificity) for detection of cardiomyopathy at both LVEF categories. The AI-enabled digital stethoscope had an AUC of 0.98 (95% CI: 0.95, 1.00) and 0.97 (95% CI: 0.93, 1.00), for detection of LVEF <45% and <50%, respectively, with 100% sensitivity and 90% specificity. Conclusion We demonstrate an AI-ECG and AI-enabled digital stethoscope were effective for detecting cardiac dysfunction in an obstetric population. Larger studies, including an evaluation of the impact of screening on clinical outcomes, are essential next steps.
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Affiliation(s)
| | | | - Heather Hardway
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Patrick Johnson
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Kathleen A Young
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | | | | | - Carl H Rose
- Department of Obstetrics and Gynecology, Mayo Clinic, Rochester, Minnesota
| | - David Burnette
- Department of Obstetrics and Gynecology, Mayo Clinic, Rochester, Minnesota
| | | | - Mia Fussell
- Agape Community Health Center, Jacksonville, Florida
| | - Sabrina Phillips
- Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, Florida
| | | | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | - Rickey E Carter
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
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13
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Dhingra LS, Aminorroaya A, Camargos AP, Khunte A, Sangha V, McIntyre D, Chow CK, Asselbergs FW, Brant LC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Electrocardiographic Signals: A Multinational Assessment. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.27.24307952. [PMID: 38854022 PMCID: PMC11160804 DOI: 10.1101/2024.05.27.24307952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Importance Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) can enable large-scale community-based risk assessment. Objective To evaluate an artificial intelligence (AI) algorithm to predict HF risk from noisy single-lead ECGs. Design Multicohort study. Setting Retrospective cohort of individuals with outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Participants Individuals without HF at baseline. Exposures AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD). Main Outcomes and Measures Among individuals with ECGs, we isolated lead I ECGs and deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated the association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against the pooled cohort equations to prevent HF (PCP-HF) score for new-onset HF using Harrel's C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results There were 194,340 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58%]), 42,741 UKB participants (65 years [59-71], 21,795 women [52%]), and 13,454 ELSA-Brasil participants (56 years [41-69], 7,348 women [55%]) with baseline ECGs. A total of 3,929 developed HF in YNHHS over 4.5 years (2.6-6.6), 46 in UKB over 3.1 years (2.1-4.5), and 31 in ELSA-Brasil over 4.2 years (3.7-4.5). A positive AI-ECG screen was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability portended a 27-65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.725 in YNHHS, 0.792 in UKB, and 0.833 in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions in addition to PCP-HF resulted in improved Harrel's C-statistic (Δ=0.112-0.114), with an IDI of 0.078-0.238 and an NRI of 20.1%-48.8% for AI-ECG vs. PCP-HF. Conclusions and Relevance Across multinational cohorts, a noise-adapted AI model with lead I ECGs as the sole input defined HF risk, representing a scalable portable and wearable device-based HF risk-stratification strategy.
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Affiliation(s)
- Lovedeep S Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Aline Pedroso Camargos
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Akshay Khunte
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Veer Sangha
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Daniel McIntyre
- Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia
| | - Clara K Chow
- Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia
- Department of Cardiology, Westmead Hospital, Sydney, Australia
| | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Luisa Cc Brant
- Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Sandhi M Barreto
- Department of Preventive Medicine, School of Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Antonio Luiz P Ribeiro
- Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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14
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Stamate E, Piraianu AI, Ciobotaru OR, Crassas R, Duca O, Fulga A, Grigore I, Vintila V, Fulga I, Ciobotaru OC. Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years. Diagnostics (Basel) 2024; 14:1103. [PMID: 38893630 PMCID: PMC11172021 DOI: 10.3390/diagnostics14111103] [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: 04/22/2024] [Revised: 05/12/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. RESULTS We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. CONCLUSIONS The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.
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Affiliation(s)
- Elena Stamate
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Alin-Ionut Piraianu
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Oana Roxana Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
| | - Rodica Crassas
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Oana Duca
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Ionica Grigore
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Vlad Vintila
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Clinical Department of Cardio-Thoracic Pathology, University of Medicine and Pharmacy “Carol Davila” Bucharest, 37 Dionisie Lupu Street, 4192910 Bucharest, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Octavian Catalin Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
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15
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Mangold KE, Carter RE, Siontis KC, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Friedman PA, Attia ZI. Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:314-323. [PMID: 38774362 PMCID: PMC11104462 DOI: 10.1093/ehjdh/ztae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 03/05/2024] [Accepted: 03/12/2024] [Indexed: 05/24/2024]
Abstract
Aims Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. Methods and results We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. Conclusion The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.
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Affiliation(s)
- Kathryn E Mangold
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | - Rickey E Carter
- Department of Quantitative Health Sciences, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA
| | | | - Peter A Noseworthy
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | | | - Samuel J Asirvatham
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | - Paul A Friedman
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
| | - Zachi I Attia
- Department of Cardiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA
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16
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Medhi D, Kamidi SR, Mamatha Sree KP, Shaikh S, Rasheed S, Thengu Murichathil AH, Nazir Z. Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review. Cureus 2024; 16:e59661. [PMID: 38836155 PMCID: PMC11148729 DOI: 10.7759/cureus.59661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/04/2024] [Indexed: 06/06/2024] Open
Abstract
Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.
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Affiliation(s)
- Diptiman Medhi
- Internal Medicine, Gauhati Medical College and Hospital, Guwahati, Guwahati, IND
| | | | | | - Shifa Shaikh
- Cardiology, SMBT Institute of Medical Sciences and Research Centre, Igatpuri, IND
| | - Shanida Rasheed
- Emergency Medicine, East Sussex Healthcare NHS Trust, Eastbourne, GBR
| | | | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, Quetta, PAK
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17
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Dhingra LS, Aminorroaya A, Sangha V, Camargos AP, Asselbergs FW, Brant LCC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.02.24305232. [PMID: 38633808 PMCID: PMC11023679 DOI: 10.1101/2024.04.02.24305232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Background Current risk stratification strategies for heart failure (HF) risk require either specific blood-based biomarkers or comprehensive clinical evaluation. In this study, we evaluated the use of artificial intelligence (AI) applied to images of electrocardiograms (ECGs) to predict HF risk. Methods Across multinational longitudinal cohorts in the integrated Yale New Haven Health System (YNHHS) and in population-based UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we identified individuals without HF at baseline. Incident HF was defined based on the first occurrence of an HF hospitalization. We evaluated an AI-ECG model that defines the cross-sectional probability of left ventricular dysfunction from a single image of a 12-lead ECG and its association with incident HF. We accounted for the competing risk of death using the Fine-Gray subdistribution model and evaluated the discrimination using Harrel's c-statistic. The pooled cohort equations to prevent HF (PCP-HF) were used as a comparator for estimating incident HF risk. Results Among 231,285 individuals at YNHHS, 4472 had a primary HF hospitalization over 4.5 years (IQR 2.5-6.6) of follow-up. In UKB and ELSA-Brasil, among 42,741 and 13,454 people, 46 and 31 developed HF over a follow-up of 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years, respectively. A positive AI-ECG screen portended a 4-fold higher risk of incident HF among YNHHS patients (age-, sex-adjusted HR [aHR] 3.88 [95% CI, 3.63-4.14]). In UKB and ELSA-Brasil, a positive-screen ECG portended 13- and 24-fold higher hazard of incident HF, respectively (aHR: UKBB, 12.85 [6.87-24.02]; ELSA-Brasil, 23.50 [11.09-49.81]). The association was consistent after accounting for comorbidities and the competing risk of death. Higher model output probabilities were progressively associated with a higher risk for HF. The model's discrimination for incident HF was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. Across cohorts, incorporating model probability with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone. Conclusions An AI model applied to images of 12-lead ECGs can identify those at elevated risk of HF across multinational cohorts. As a digital biomarker of HF risk that requires just an ECG image, this AI-ECG approach can enable scalable and efficient screening for HF risk.
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Affiliation(s)
- Lovedeep S Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Veer Sangha
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Aline Pedroso Camargos
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Luisa CC Brant
- Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Sandhi M Barreto
- Department of Preventive Medicine, School of Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Antonio Luiz P Ribeiro
- Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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18
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Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1028-e1050. [PMID: 38415358 PMCID: PMC11042786 DOI: 10.1161/cir.0000000000001201] [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] [Indexed: 02/29/2024]
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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19
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Marsden H, Kemos P, Venzi M, Noy M, Maheswaran S, Francis N, Hyde C, Mullarkey D, Kalsi D, Thomas L. Accuracy of an artificial intelligence as a medical device as part of a UK-based skin cancer teledermatology service. Front Med (Lausanne) 2024; 11:1302363. [PMID: 38585154 PMCID: PMC10996444 DOI: 10.3389/fmed.2024.1302363] [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: 09/26/2023] [Accepted: 02/27/2024] [Indexed: 04/09/2024] Open
Abstract
Introduction An artificial intelligence as a medical device (AIaMD), built on convolutional neural networks, has demonstrated high sensitivity for melanoma. To be of clinical value, it needs to safely reduce referral rates. The primary objective of this study was to demonstrate that the AIaMD had a higher rate of correctly classifying lesions that did not need to be referred for biopsy or urgent face-to-face dermatologist review, compared to teledermatology standard of care (SoC), while achieving the same sensitivity to detect malignancy. Secondary endpoints included the sensitivity, specificity, positive and negative predictive values, and number needed to biopsy to identify one case of melanoma or squamous cell carcinoma (SCC) by both the AIaMD and SoC. Methods This prospective, single-centre, single-arm, masked, non-inferiority, adaptive, group sequential design trial recruited patients referred to a teledermatology cancer pathway (clinicaltrials.gov NCT04123678). Additional dermoscopic images of each suspicious lesion were taken using a smartphone with a dermoscopic lens attachment. The images were assessed independently by a consultant dermatologist and the AIaMD. The outputs were compared with the final histological or clinical diagnosis. Results A total of 700 patients with 867 lesions were recruited, of which 622 participants with 789 lesions were included in the per-protocol (PP) population. In total, 63.3% of PP participants were female; 89.0% identified as white, and the median age was 51 (range 18-95); and all Fitzpatrick skin types were represented including 25/622 (4.0%) type IV-VI skin. A total of 67 malignant lesions were identified, including 8 diagnosed as melanoma. The AIaMD sensitivity was set at 91 and 92.5%, to match the literature-defined clinician sensitivity (91.46%) as closely as possible. In both settings, the AIaMD identified had a significantly higher rate of identifying lesions that did not need a biopsy or urgent referral compared to SoC (p-value = 0.001) with comparable sensitivity for skin cancer. Discussion The AIaMD identified significantly more lesions that did not need to be referred for biopsy or urgent face-to-face dermatologist review, compared to teledermatologists. This has the potential to reduce the burden of unnecessary referrals when used as part of a teledermatology service.
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Affiliation(s)
| | - Polychronis Kemos
- Blizard Institute, The Faculty of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | | | - Mariana Noy
- Chelsea and Westminster Hospital NHS Foundation Trust, London, United Kingdom
| | - Shameera Maheswaran
- Chelsea and Westminster Hospital NHS Foundation Trust, London, United Kingdom
| | - Nicholas Francis
- Imperial College Healthcare NHS Trust, St Mary's Hospital, London, United Kingdom
| | - Christopher Hyde
- Exeter Test Group, Department of Health and Community Sciences, University of Exeter Medical School, Exeter, United Kingdom
| | | | | | - Lucy Thomas
- Chelsea and Westminster Hospital NHS Foundation Trust, London, United Kingdom
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20
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Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
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Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
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21
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Makimoto H, Kohro T. Adopting artificial intelligence in cardiovascular medicine: a scoping review. Hypertens Res 2024; 47:685-699. [PMID: 37907600 DOI: 10.1038/s41440-023-01469-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/03/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.
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Affiliation(s)
- Hisaki Makimoto
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.
| | - Takahide Kohro
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan
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22
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Varma N, Han JK, Passman R, Rosman LA, Ghanbari H, Noseworthy P, Avari Silva JN, Deshmukh A, Sanders P, Hindricks G, Lip G, Sridhar AR. Promises and Perils of Consumer Mobile Technologies in Cardiovascular Care: JACC Scientific Statement. J Am Coll Cardiol 2024; 83:611-631. [PMID: 38296406 DOI: 10.1016/j.jacc.2023.11.024] [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: 11/01/2023] [Accepted: 11/16/2023] [Indexed: 02/08/2024]
Abstract
Direct-to-consumer (D2C) wearables are becoming increasingly popular in cardiovascular health management because of their affordability and capability to capture diverse health data. Wearables may enable continuous health care provider-patient partnerships and reduce the volume of episodic clinic-based care (thereby reducing health care costs). However, challenges arise from the unregulated use of these devices, including questionable data reliability, potential misinterpretation of information, unintended psychological impacts, and an influx of clinically nonactionable data that may overburden the health care system. Further, these technologies could exacerbate, rather than mitigate, health disparities. Experience with wearables in atrial fibrillation underscores these challenges. The prevalent use of D2C wearables necessitates a collaborative approach among stakeholders to ensure effective integration into cardiovascular care. Wearables are heralding innovative disease screening, diagnosis, and management paradigms, expanding therapeutic avenues, and anchoring personalized medicine.
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Affiliation(s)
- Niraj Varma
- Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio, USA.
| | - Janet K Han
- Department of Cardiology, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA; Department of Cardiology, David Geffen School of Medicine at the University of California-Los Angeles, Los Angeles, California, USA
| | - Rod Passman
- Department of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Lindsey Anne Rosman
- Division of Cardiology, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA
| | - Hamid Ghanbari
- Department of Cardiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Peter Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Abhishek Deshmukh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Prashanthan Sanders
- Department of Cardiology, University of Adelaide, South Australia, Australia
| | | | - Gregory Lip
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University, and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom; Department of Clinical Medicine, Danish Center for Clinical Health Services Research, Aalborg University, Aalborg, Denmark
| | - Arun R Sridhar
- Department of Cardiology, Pulse Heart Institute, Seattle, Washington, USA; Department of Clinical Medicine, Danish Center for Clinical Health Services Research, Aalborg University, Aalborg, Denmark
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23
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Ichihara YK, Kohsaka S, Kisanuki M, Sandhu ATS, Kawana M. Implementation of evidence-based heart failure management: Regional variations between Japan and the USA. J Cardiol 2024; 83:74-83. [PMID: 37543194 DOI: 10.1016/j.jjcc.2023.07.019] [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: 03/31/2023] [Revised: 07/26/2023] [Accepted: 07/28/2023] [Indexed: 08/07/2023]
Abstract
The implementation of optimal medical therapy is a crucial step in the management of heart failure with reduced ejection fraction (HFrEF). Over the prior three decades, there have been substantial advancements in this field. Early and accurate detection and diagnosis of the disease allow for the appropriate initiation of optimal therapies. The initiation and uptitration of optimal medical therapy including renin-angiotensin system inhibitor, beta-blocker, mineralocorticoid receptor antagonist, and sodium-glucose cotransporter 2 inhibitor in the early stage would prevent the progression and morbidity of HF. Concurrently, individualized surveillance to recognize and treat signs of disease progression is critical given the progressive nature of HF, even among stable patients on optimal therapy. However, there remains a wide variation in regional practice regarding the initiation, titration, and long-term monitoring of this therapy. To cover the differences in approaches toward HFrEF management and the implementation of guideline-based medical therapy, we discuss the current evidence in this arena, differences in present guideline recommendations, and compare practice patterns in Japan and the USA using a case of new-onset HF as an example. We will discuss pros and cons of the way HF is managed in each region, and highlight potential areas for improvement in care.
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Affiliation(s)
| | - Shun Kohsaka
- Department of Cardiology, Keio University School of Medicine, Tokyo, Japan
| | - Megumi Kisanuki
- Department of Medicine and Biosystemic Sciences, Kyushu University Graduate School of Medicine, Fukuoka, Japan
| | | | - Masataka Kawana
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
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24
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Shiraga T, Makimoto H, Kohlmann B, Magnisali CE, Imai Y, Itani Y, Makimoto A, Schölzel F, Bejinariu A, Kelm M, Rana O. Improving Valvular Pathologies and Ventricular Dysfunction Diagnostic Efficiency Using Combined Auscultation and Electrocardiography Data: A Multimodal AI Approach. SENSORS (BASEL, SWITZERLAND) 2023; 23:9834. [PMID: 38139680 PMCID: PMC10748155 DOI: 10.3390/s23249834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/07/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
Simple sensor-based procedures, including auscultation and electrocardiography (ECG), can facilitate early diagnosis of valvular diseases, resulting in timely treatment. This study assessed the impact of combining these sensor-based procedures with machine learning on diagnosing valvular abnormalities and ventricular dysfunction. Data from auscultation at three distinct locations and 12-lead ECGs were collected from 1052 patients undergoing echocardiography. An independent cohort of 103 patients was used for clinical validation. These patients were screened for severe aortic stenosis (AS), severe mitral regurgitation (MR), and left ventricular dysfunction (LVD) with ejection fractions ≤ 40%. Optimal neural networks were identified by a fourfold cross-validation training process using heart sounds and various ECG leads, and their outputs were combined using a stacking technique. This composite sensor model had high diagnostic efficiency (area under the receiver operating characteristic curve (AUC) values: AS, 0.93; MR, 0.80; LVD, 0.75). Notably, the contribution of individual sensors to disease detection was found to be disease-specific, underscoring the synergistic potential of the sensor fusion approach. Thus, machine learning models that integrate auscultation and ECG can efficiently detect conditions typically diagnosed via imaging. Moreover, this study highlights the potential of multimodal artificial intelligence applications.
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Affiliation(s)
| | - Hisaki Makimoto
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke-City 329-0498, Japan
| | - Benita Kohlmann
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
| | - Christofori-Eleni Magnisali
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
| | - Yoshie Imai
- Mitsubishi Electric Inc., Kamakura 247-0056, Japan
| | - Yusuke Itani
- Mitsubishi Electric Inc., Kamakura 247-0056, Japan
| | - Asuka Makimoto
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
| | - Fabian Schölzel
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
| | - Alexandru Bejinariu
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
| | - Malte Kelm
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
- CARID—Cardiovascular Research Institute Düsseldorf, 40225 Düsseldorf, Germany
| | - Obaida Rana
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
- CARID—Cardiovascular Research Institute Düsseldorf, 40225 Düsseldorf, Germany
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Slabaugh G, Beltran L, Rizvi H, Deloukas P, Marouli E. Applications of machine and deep learning to thyroid cytology and histopathology: a review. Front Oncol 2023; 13:958310. [PMID: 38023130 PMCID: PMC10661921 DOI: 10.3389/fonc.2023.958310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/12/2023] [Indexed: 12/01/2023] Open
Abstract
This review synthesises past research into how machine and deep learning can improve the cyto- and histopathology processing pipelines for thyroid cancer diagnosis. The current gold-standard preoperative technique of fine-needle aspiration cytology has high interobserver variability, often returns indeterminate samples and cannot reliably identify some pathologies; histopathology analysis addresses these issues to an extent, but it requires surgical resection of the suspicious lesions so cannot influence preoperative decisions. Motivated by these issues, as well as by the chronic shortage of trained pathologists, much research has been conducted into how artificial intelligence could improve current pipelines and reduce the pressure on clinicians. Many past studies have indicated the significant potential of automated image analysis in classifying thyroid lesions, particularly for those of papillary thyroid carcinoma, but these have generally been retrospective, so questions remain about both the practical efficacy of these automated tools and the realities of integrating them into clinical workflows. Furthermore, the nature of thyroid lesion classification is significantly more nuanced in practice than many current studies have addressed, and this, along with the heterogeneous nature of processing pipelines in different laboratories, means that no solution has proven itself robust enough for clinical adoption. There are, therefore, multiple avenues for future research: examine the practical implementation of these algorithms as pathologist decision-support systems; improve interpretability, which is necessary for developing trust with clinicians and regulators; and investigate multiclassification on diverse multicentre datasets, aiming for methods that demonstrate high performance in a process- and equipment-agnostic manner.
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Affiliation(s)
- Greg Slabaugh
- Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom
| | - Luis Beltran
- Barts Health NHS Trust, The Royal London Hospital, London, United Kingdom
| | - Hasan Rizvi
- Barts Health NHS Trust, The Royal London Hospital, London, United Kingdom
| | - Panos Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Eirini Marouli
- Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom
- Barts Health NHS Trust, The Royal London Hospital, London, United Kingdom
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
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26
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Xie M, Zhu S, Liu G, Wu Y, Zhou W, Yu D, Wan J, Xing S, Wang S, Gan L, Li G, Chang D, Lai H, Liu N, Zhu P. A Novel Quantitative Electrocardiography Strategy Reveals the Electroinhibitory Effect of Tamoxifen on the Mouse Heart. J Cardiovasc Transl Res 2023; 16:1232-1248. [PMID: 37155136 DOI: 10.1007/s12265-023-10395-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 04/26/2023] [Indexed: 05/10/2023]
Abstract
Tamoxifen, a selective estrogen receptor modulator, was initially used to treat cancer in women and more recently to induce conditional gene editing in rodent hearts. However, little is known about the baseline biological effects of tamoxifen on the myocardium. In order to clarify the short-term effects of tamoxifen on cardiac electrophysiology of myocardium, we applied a single-chest-lead quantitative method and analyzed the short-term electrocardiographic phenotypes induced by tamoxifen in the heart of adult female mice. We found that tamoxifen prolonged the PP interval and caused a decreased heartbeat, and further induced atrioventricular block by gradually prolonging the PR interval. Further correlation analysis suggested that tamoxifen had a synergistic and dose-independent inhibition on the time course of the PP interval and PR interval. This prolongation of the critical time course may represent a tamoxifen-specific ECG excitatory-inhibitory mechanism, leading to a reduction in the number of supraventricular action potentials and thus bradycardia. Segmental reconstructions showed that tamoxifen induced a decrease in the conduction velocity of action potentials throughout the atria and parts of the ventricles, resulting in a flattening of the P wave and R wave. In addition, we detected the previously reported prolongation of the QT interval, which may be due to a prolonged duration of the ventricular repolarizing T wave rather than the depolarizing QRS complex. Our study highlights that tamoxifen can produce patterning alternations in the cardiac conduction system, including the formation of inhibitory electrical signals with reduced conduction velocity, implying its involvement in the regulation of myocardial ion transport and the mediation of arrhythmias. A Novel Quantitative Electrocardiography Strategy Reveals the Electroinhibitory Effect of Tamoxifen on the Mouse Heart(Figure 9). A working model of tamoxifen producing acute electrical disturbances in the myocardium. SN, sinus node; AVN, atrioventricular node; RA, right atrium; LA, left atrium; RV, right ventricle; LV, left ventricle.
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Affiliation(s)
- Ming Xie
- Department of Cardiac Surgery, School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China
| | - Shuoji Zhu
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China
- University of Tokyo, Tokyo, 113-8666, Japan
| | - Gang Liu
- Department of Cardiac Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yijin Wu
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China
| | - Wenkai Zhou
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China
| | - Dingdang Yu
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China
| | - Jinkai Wan
- Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Shenghui Xing
- Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Siqing Wang
- Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Lin Gan
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China
| | - Ge Li
- Department of Cardiac Surgery, School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China
| | - Dehua Chang
- University of Tokyo Hospital Department of Cell Therapy in Regenerative Medicine, Tokyo, 113-8666, Japan.
| | - Hao Lai
- Department of Cardiac Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
| | - Nanbo Liu
- Department of Cardiac Surgery, School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China.
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China.
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China.
| | - Ping Zhu
- Department of Cardiac Surgery, School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China.
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China.
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China.
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Krishnan G, Singh S, Pathania M, Gosavi S, Abhishek S, Parchani A, Dhar M. Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm. Front Artif Intell 2023; 6:1227091. [PMID: 37705603 PMCID: PMC10497111 DOI: 10.3389/frai.2023.1227091] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/09/2023] [Indexed: 09/15/2023] Open
Abstract
As the demand for quality healthcare increases, healthcare systems worldwide are grappling with time constraints and excessive workloads, which can compromise the quality of patient care. Artificial intelligence (AI) has emerged as a powerful tool in clinical medicine, revolutionizing various aspects of patient care and medical research. The integration of AI in clinical medicine has not only improved diagnostic accuracy and treatment outcomes, but also contributed to more efficient healthcare delivery, reduced costs, and facilitated better patient experiences. This review article provides an extensive overview of AI applications in history taking, clinical examination, imaging, therapeutics, prognosis and research. Furthermore, it highlights the critical role AI has played in transforming healthcare in developing nations.
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Affiliation(s)
- Gokul Krishnan
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Shiana Singh
- Department of Emergency Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Monika Pathania
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Siddharth Gosavi
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Shuchi Abhishek
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Ashwin Parchani
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Minakshi Dhar
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
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28
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Sau A, Ng FS. --The emerging role of artificial intelligence enabled electrocardiograms in healthcare. BMJ MEDICINE 2023; 2:e000193. [PMID: 37841970 PMCID: PMC10568120 DOI: 10.1136/bmjmed-2022-000193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 06/01/2023] [Indexed: 10/17/2023]
Affiliation(s)
- Arunashis Sau
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK
- Department of Cardiology, Chelsea and Westminster Hospital NHS Foundation Trust, London, UK
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29
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Khunte A, Sangha V, Oikonomou EK, Dhingra LS, Aminorroaya A, Mortazavi BJ, Coppi A, Brandt CA, Krumholz HM, Khera R. Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices. NPJ Digit Med 2023; 6:124. [PMID: 37433874 PMCID: PMC10336107 DOI: 10.1038/s41746-023-00869-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 06/26/2023] [Indexed: 07/13/2023] Open
Abstract
Artificial intelligence (AI) can detect left ventricular systolic dysfunction (LVSD) from electrocardiograms (ECGs). Wearable devices could allow for broad AI-based screening but frequently obtain noisy ECGs. We report a novel strategy that automates the detection of hidden cardiovascular diseases, such as LVSD, adapted for noisy single-lead ECGs obtained on wearable and portable devices. We use 385,601 ECGs for development of a standard and noise-adapted model. For the noise-adapted model, ECGs are augmented during training with random gaussian noise within four distinct frequency ranges, each emulating real-world noise sources. Both models perform comparably on standard ECGs with an AUROC of 0.90. The noise-adapted model performs significantly better on the same test set augmented with four distinct real-world noise recordings at multiple signal-to-noise ratios (SNRs), including noise isolated from a portable device ECG. The standard and noise-adapted models have an AUROC of 0.72 and 0.87, respectively, when evaluated on ECGs augmented with portable ECG device noise at an SNR of 0.5. This approach represents a novel strategy for the development of wearable-adapted tools from clinical ECG repositories.
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Affiliation(s)
- Akshay Khunte
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Veer Sangha
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Lovedeep S Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Bobak J Mortazavi
- Department of Computer Science & Engineering, Texas A&M University, College Station, TX, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Andreas Coppi
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Cynthia A Brandt
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA.
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
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30
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Zepeda-Echavarria A, van de Leur RR, van Sleuwen M, Hassink RJ, Wildbergh TX, Doevendans PA, Jaspers J, van Es R. Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review. JMIR Cardio 2023; 7:e44003. [PMID: 37418308 PMCID: PMC10362423 DOI: 10.2196/44003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 03/29/2023] [Accepted: 06/06/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND Electrocardiograms (ECGs) are used by physicians to record, monitor, and diagnose the electrical activity of the heart. Recent technological advances have allowed ECG devices to move out of the clinic and into the home environment. There is a great variety of mobile ECG devices with the capabilities to be used in home environments. OBJECTIVE This scoping review aimed to provide a comprehensive overview of the current landscape of mobile ECG devices, including the technology used, intended clinical use, and available clinical evidence. METHODS We conducted a scoping review to identify studies concerning mobile ECG devices in the electronic database PubMed. Secondarily, an internet search was performed to identify other ECG devices available in the market. We summarized the devices' technical information and usability characteristics based on manufacturer data such as datasheets and user manuals. For each device, we searched for clinical evidence on the capabilities to record heart disorders by performing individual searches in PubMed and ClinicalTrials.gov, as well as the Food and Drug Administration (FDA) 510(k) Premarket Notification and De Novo databases. RESULTS From the PubMed database and internet search, we identified 58 ECG devices with available manufacturer information. Technical characteristics such as shape, number of electrodes, and signal processing influence the capabilities of the devices to record cardiac disorders. Of the 58 devices, only 26 (45%) had clinical evidence available regarding their ability to detect heart disorders such as rhythm disorders, more specifically atrial fibrillation. CONCLUSIONS ECG devices available in the market are mainly intended to be used for the detection of arrhythmias. No devices are intended to be used for the detection of other cardiac disorders. Technical and design characteristics influence the intended use of the devices and use environments. For mobile ECG devices to be intended to detect other cardiac disorders, challenges regarding signal processing and sensor characteristics should be solved to increase their detection capabilities. Devices recently released include the use of other sensors on ECG devices to increase their detection capabilities.
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Affiliation(s)
- Alejandra Zepeda-Echavarria
- Medical Technologies and Clinical Physics, Facilitation Department, University Medical Center Utrecht, Utrecht, Netherlands
| | - Rutger R van de Leur
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, Netherlands
| | - Meike van Sleuwen
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, Netherlands
| | - Rutger J Hassink
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Pieter A Doevendans
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, Netherlands
- HeartEye BV, Delft, Netherlands
- Netherlands Heart Institute, Utrecht, Netherlands
| | - Joris Jaspers
- Medical Technologies and Clinical Physics, Facilitation Department, University Medical Center Utrecht, Utrecht, Netherlands
| | - René van Es
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, Netherlands
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Morales-Lara AC, Garzon-Siatoya WT, Fernandez-Campos BA, Adedinsewo D. Advancing Our Understanding of Women's Cardiovascular Health Through Digital Health and Artificial Intelligence. JACC. ADVANCES 2023; 2:100272. [PMID: 38938318 PMCID: PMC11198228 DOI: 10.1016/j.jacadv.2023.100272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Affiliation(s)
| | | | | | - Demilade Adedinsewo
- Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, Florida, USA
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32
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Bachtiger P, Kelshiker MA, Petri CF, Gandhi M, Shah M, Kamalati T, Khan SA, Hooper G, Stephens J, Alrumayh A, Barton C, Kramer DB, Plymen CM, Peters NS. Survival and health economic outcomes in heart failure diagnosed at hospital admission versus community settings: a propensity-matched analysis. BMJ Health Care Inform 2023; 30:bmjhci-2022-100718. [PMID: 36921978 PMCID: PMC10030479 DOI: 10.1136/bmjhci-2022-100718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/28/2023] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND AND AIMS Most patients with heart failure (HF) are diagnosed following a hospital admission. The clinical and health economic impacts of index HF diagnosis made on admission to hospital versus community settings are not known. METHODS We used the North West London Discover database to examine 34 208 patients receiving an index diagnosis of HF between January 2015 and December 2020. A propensity score-matched (PSM) cohort was identified to adjust for differences in socioeconomic status, cardiovascular risk and pre-diagnosis health resource utilisation cost. Outcomes were stratified by two pathways to index HF diagnosis: a 'hospital pathway' was defined by diagnosis following hospital admission; and a 'community pathway' by diagnosis via a general practitioner or outpatient services. The primary clinical and health economic endpoints were all-cause mortality and cost-consequence differential, respectively. RESULTS The diagnosis of HF was via hospital pathway in 68% (23 273) of patients. The PSM cohort included 17 174 patients (8582 per group) and was matched across all selected confounders (p>0.05). The ratio of deaths per person-months at 24 months comparing community versus hospital diagnosis was 0.780 (95% CI 0.722 to 0.841, p<0.0001). By 72 months, the ratio of deaths was 0.960 (0.905 to 1.020, p=0.18). Diagnosis via hospital pathway incurred an overall extra longitudinal cost of £2485 per patient. CONCLUSIONS Index diagnosis of HF through hospital admission continues to dominate and is associated with a significantly greater short-term risk of mortality and substantially increased long-term costs than if first diagnosed in the community. This study highlights the potential for community diagnosis-early, before symptoms necessitate hospitalisation-to improve both clinical and health economic outcomes.
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Affiliation(s)
- Patrik Bachtiger
- National Heart and Lung Institue, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Mihir A Kelshiker
- National Heart and Lung Institue, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Camille F Petri
- National Heart and Lung Institue, Imperial College London, London, UK
| | - Manisha Gandhi
- National Heart and Lung Institue, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | | | | | | | | | - Jon Stephens
- Upstart Breakthrough Strategy Limited, London, UK
| | - Abdullah Alrumayh
- National Heart and Lung Institue, Imperial College London, London, UK
| | - Carys Barton
- Imperial College Healthcare NHS Trust, London, UK
| | - Daniel B Kramer
- National Heart and Lung Institue, Imperial College London, London, UK
- Harvard Medical School, Boston, Massachusetts, USA
| | | | - Nicholas S Peters
- National Heart and Lung Institue, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
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33
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De Michieli L, Knott JD, Attia ZI, Ola O, Mehta RA, Akula A, Hodge DO, Gulati R, Friedman PA, Jaffe AS, Sandoval Y. Artificial intelligence-augmented electrocardiography for left ventricular systolic dysfunction in patients undergoing high-sensitivity cardiac troponin T. EUROPEAN HEART JOURNAL. ACUTE CARDIOVASCULAR CARE 2023; 12:106-114. [PMID: 36537652 DOI: 10.1093/ehjacc/zuac156] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 12/06/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022]
Abstract
AIMS Our goal was to evaluate a previously validated artificial intelligence-augmented electrocardiography (AI-ECG) screening tool for left ventricular systolic dysfunction (LVSD) in patients undergoing high-sensitivity-cardiac troponin T (hs-cTnT). METHODS AND RESULTS Retrospective application of AI-ECG for LVSD in emergency department (ED) patients undergoing hs-cTnT. AI-ECG scores (0-1) for probability of LVSD (left ventricular ejection fraction ≤ 35%) were obtained. An AI-ECG score ≥0.256 indicates a positive screen. The primary endpoint was a composite of post-discharge major adverse cardiovascular events (MACEs) at two years follow-up. Among 1977 patients, 248 (13%) had a positive AI-ECG. When compared with patients with a negative AI-ECG, those with a positive AI-ECG had a higher risk for MACE [48 vs. 21%, P < 0.0001, adjusted hazard ratio (HR) 1.39, 95% confidence interval (CI) 1.11-1.75]. This was largely because of a higher rate of deaths (32 vs. 14%, P < 0.0001; adjusted HR 1.26, 95% 0.95-1.66) and heart failure hospitalizations (26 vs. 6.1%, P < 0.001; adjusted HR 1.75, 95% CI 1.25-2.45). Together, hs-cTnT and AI-ECG resulted in the following MACE rates and adjusted HRs: hs-cTnT < 99th percentile and negative AI-ECG: 116/1176 (11%; reference), hs-cTnT < 99th percentile and positive AI-ECG: 28/107 (26%; adjusted HR 1.54, 95% CI 1.01-2.36), hs-cTnT > 99th percentile and negative AI-ECG: 233/553 (42%; adjusted HR 2.12, 95% CI 1.66, 2.70), and hs-cTnT > 99th percentile and positive AI-ECG: 91/141 (65%; adjusted HR 2.83, 95% CI 2.06, 3.87). CONCLUSION Among ED patients evaluated with hs-cTnT, a positive AI-ECG for LVSD identifies patients at high risk for MACE. The conjoint use of hs-cTnT and AI-ECG facilitates risk stratification.
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Affiliation(s)
- Laura De Michieli
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA.,Department of Cardiac, Thoracic and Vascular Sciences and Public Health, University of Padova, Padova, Italy
| | - Jonathan D Knott
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Zachi I Attia
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Olatunde Ola
- Department of Hospital Internal Medicine, Mayo Clinic Health System, La Crosse, WI, USA.,Center for Clinical and Translational Science, Mayo Clinic Graduate School of Biomedical Sciences, Rochester MN, USA
| | - Ramila A Mehta
- Department of Quantitative Health Sciences, Mayo College of Medicine, Rochester, MN, USA
| | - Ashok Akula
- Department of Hospital Internal Medicine, Mayo Clinic Health System, La Crosse, WI, USA.,Center for Clinical and Translational Science, Mayo Clinic Graduate School of Biomedical Sciences, Rochester MN, USA
| | - David O Hodge
- Department of Quantitative Health Sciences, Mayo College of Medicine, Jacksonville, FL, USA
| | - Rajiv Gulati
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Paul A Friedman
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Allan S Jaffe
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA.,Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Yader Sandoval
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA.,Interventional Section, Minneapolis Heart Institute, Abbott Northwestern Hospital and Minneapolis Heart Institute Foundation, 920 E 28th Street Suite 300, Minneapolis, MN 55407, USA
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Angelaki E, Barmparis GD, Kochiadakis G, Maragkoudakis S, Savva E, Kampanieris E, Kassotakis S, Kalomoirakis P, Vardas P, Tsironis GP, Marketou ME. Artificial intelligence-based opportunistic screening for the detection of arterial hypertension through ECG signals. J Hypertens 2022; 40:2494-2501. [PMID: 36189460 DOI: 10.1097/hjh.0000000000003286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
OBJECTIVES Hypertension is a major risk factor for cardiovascular disease (CVD), which often escapes the diagnosis or should be confirmed by several office visits. The ECG is one of the most widely used diagnostic tools and could be of paramount importance in patients' initial evaluation. METHODS We used machine learning techniques based on clinical parameters and features derived from the ECG, to detect hypertension in a population without CVD. We enrolled 1091 individuals who were classified as hypertensive or normotensive, and trained a Random Forest model, to detect the existence of hypertension. We then calculated the values for the Shapley additive explanations (SHAP), a sophisticated feature importance analysis, to interpret each feature's role in the Random Forest's results. RESULTS Our Random Forest model was able to distinguish hypertensive from normotensive patients with accuracy 84.2%, specificity 78.0%, sensitivity 84.0% and area under the receiver-operating curve 0.89, using a decision threshold of 0.6. Age, BMI, BMI-adjusted Cornell criteria (BMI multiplied by RaVL+SV 3 ), R wave amplitude in aVL and BMI-modified Sokolow-Lyon voltage (BMI divided by SV 1 +RV 5 ), were the most important anthropometric and ECG-derived features in terms of the success of our model. CONCLUSION Our machine learning algorithm is effective in the detection of hypertension in patients using ECG-derived and basic anthropometric criteria. Our findings open new horizon in the detection of many undiagnosed hypertensive individuals who have an increased CVD risk.
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Affiliation(s)
- Eleni Angelaki
- Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Crete, Greece
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Georgios D Barmparis
- Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Crete, Greece
| | - George Kochiadakis
- Department of Cardiology, Heraklion University Hospital, Heraklion, Greece
| | | | - Eirini Savva
- Department of Cardiology, Heraklion University Hospital, Heraklion, Greece
| | | | - Spyros Kassotakis
- Department of Cardiology, Heraklion University Hospital, Heraklion, Greece
| | | | - Panos Vardas
- Department of Cardiology, Heraklion University Hospital, Heraklion, Greece
- Heart Sector, Hygeia Hospitals Group, Athens, Greece
| | - Giorgos P Tsironis
- Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Crete, Greece
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Maria E Marketou
- Department of Cardiology, Heraklion University Hospital, Heraklion, Greece
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Luo H, Weerts J, Bekkers A, Achten A, Lievens S, Smeets K, van Empel V, Delhaas T, Prinzen FW. Association between phonocardiography and echocardiography in heart failure patients with preserved ejection fraction. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 4:4-11. [PMID: 36743874 PMCID: PMC9890082 DOI: 10.1093/ehjdh/ztac073] [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: 04/26/2022] [Revised: 10/06/2022] [Indexed: 11/23/2022]
Abstract
Aims Heart failure with preserved ejection fraction (HFpEF) is associated with stiffened myocardium and elevated filling pressure that may be captured by heart sound (HS). We investigated the relationship between phonocardiography (PCG) and echocardiography in symptomatic patients suspected of HFpEF. Methods and results Consecutive symptomatic patients with sinus rhythm and left ventricular ejection fraction >45% were enrolled. Echocardiography was performed to evaluate the patients' diastolic function, accompanied by PCG measurements. Phonocardiography features including HS amplitude, frequency, and timing intervals were calculated, and their abilities to differentiate the ratio between early mitral inflow velocity and early diastolic mitral annular velocity (E/e') were investigated. Of 45 patients, variable ratio matching was applied to obtain two groups of patients with similar characteristics but different E/e'. Patients with a higher E/e' showed higher first and second HS frequencies and more fourth HS and longer systolic time intervals. The interval from QRS onset to first HS was the best feature for the prediction of E/e' > 9 [area under the curve (AUC): 0.72 (0.51-0.88)] in the matched patients. In comparison, N-terminal pro-brain natriuretic peptide (NT-proBNP) showed an AUC of 0.67 (0.46-0.85), a value not better than any PCG feature (P > 0.05). Conclusion Phonocardiography features stratify E/e' in symptomatic patients suspected of HFpEF with a diagnostic performance similar to NT-proBNP. Heart sound may serve as a simple non-invasive tool for evaluating HFpEF patients.
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Affiliation(s)
| | | | - Anja Bekkers
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre (MUMC+), P. Debyelaan 25, 6229 HX Maastricht, The Netherlands
| | - Anouk Achten
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre (MUMC+), P. Debyelaan 25, 6229 HX Maastricht, The Netherlands
| | - Sien Lievens
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands,Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre (MUMC+), P. Debyelaan 25, 6229 HX Maastricht, The Netherlands
| | - Kimberly Smeets
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre (MUMC+), P. Debyelaan 25, 6229 HX Maastricht, The Netherlands
| | - Vanessa van Empel
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Centre (MUMC+), P. Debyelaan 25, 6229 HX Maastricht, The Netherlands
| | - Tammo Delhaas
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Frits W Prinzen
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
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Kim KH, Kwon JM, Pereira T, Attia ZI, Pereira NL. Artificial Intelligence Applied to Cardiomyopathies: Is It Time for Clinical Application? Curr Cardiol Rep 2022; 24:1547-1555. [PMID: 36048306 DOI: 10.1007/s11886-022-01776-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/16/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) techniques have the potential to remarkably change the practice of cardiology in order to improve and optimize outcomes in heart failure and specifically cardiomyopathies, offering us novel tools to interpret data and make clinical decisions. The aim of this review is to describe the contemporary state of AI and digital health applied to cardiomyopathies as well as to define a potential pivotal role of its application by physicians in clinical practice. RECENT FINDINGS Many studies have been undertaken in recent years on cardiomyopathy screening especially using AI-enhanced electrocardiography (ECG). Even with mild left ventricular (LV) dysfunction, AI-ECG screening for amyloidosis, hypertrophic cardiomyopathy, or dilated cardiomyopathy is now feasible. Introduction of AI-ECG in routine clinical care has resulted in higher detection of LV systolic dysfunction; however, clinical research on a broader scale with diverse populations is necessary and ongoing. In the area of cardiac-imaging, AI automatically assesses the thickness and characteristics of myocardium to differentiate cardiomyopathies, but research on its prognostic capability has yet to be conducted. AI is also being applied to cardiomyopathy genomics, especially to predict pathogenicity of variants and identify whether these variants are clinically actionable. While the implementation of AI in the diagnosis and treatment of cardiomyopathies is still in its infancy, an ever-growing clinical research strategy will ascertain the clinical utility of these AI tools to help improve diagnosis of and outcomes in cardiomyopathies. We also need to standardize the tools used to monitor the performance of AI-based systems which can then be used to expedite decision-making and rectify any hidden biases. Given its potential important role in clinical practice, healthcare providers need to familiarize themselves with the promise and limitations of this technology.
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Affiliation(s)
- Kyung-Hee Kim
- Internal Medicine, Department of Cardiology, Incheon Sejong Hospital, Incheon, South Korea.,Department of Cardiovascular Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
| | - Joon-Myung Kwon
- Department of Critical Care and Emergency Medicine, Incheon Sejong Hospital, Incheon, South Korea.,Medical Research Team, Medical AI, Co, Seoul, South Korea
| | - Tara Pereira
- Artificial Intelligence Development, Center for Digital Health, Mayo Clinic, Rochester, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
| | - Naveen L Pereira
- Department of Cardiovascular Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA. .,Department of Molecular Pharmacology and Therapeutics, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
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Huang JD, Wang J, Ramsey E, Leavey G, Chico TJA, Condell J. Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:8002. [PMID: 36298352 PMCID: PMC9610988 DOI: 10.3390/s22208002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 06/06/2023]
Abstract
Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.
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Affiliation(s)
- Jian-Dong Huang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Jinling Wang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Elaine Ramsey
- Department of Global Business & Enterprise, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Gerard Leavey
- School of Psychology, Ulster University at Coleraine, Londonderry BT52 1SA, UK
| | - Timothy J. A. Chico
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, The University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK
| | - Joan Condell
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
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Patel N, Agamy S, Ahmad M. Correspondence to the European Heart Journal-digital health in response to the paper by Attia et al. 2022. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:499. [PMID: 36710889 PMCID: PMC9779798 DOI: 10.1093/ehjdh/ztac053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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Real-world evaluation of the Stemoscope electronic tele-auscultation system. Biomed Eng Online 2022; 21:63. [PMID: 36068509 PMCID: PMC9446597 DOI: 10.1186/s12938-022-01032-4] [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: 05/16/2022] [Accepted: 08/24/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND With the spread of COVID-19, telemedicine has played an important role, but tele-auscultation is still unavailable in most countries. This study introduces and tests a tele-auscultation system (Stemoscope) and compares the concordance of the Stemoscope with the traditional stethoscope in the evaluation of heart murmurs. METHODS A total of 57 patients with murmurs were recruited, and echocardiographs were performed. Three cardiologists were asked to correctly categorize heart sounds (both systolic murmur and diastolic murmur) as normal vs. abnormal with both the Stemoscope and a traditional acoustic stethoscope under different conditions. Firstly, we compared the in-person auscultation agreement between Stemoscope and the conventional acoustic stethoscope. Secondly, we compared tele-auscultation (recorded heart sounds) agreement between Stemoscope and acoustic results. Thirdly, we compared both the Stemoscope tele-auscultation results and traditional acoustic stethoscope in-person auscultation results with echocardiography. Finally, ten other cardiologists were asked to complete a qualitative questionnaire to assess their experience using the Stemoscope. RESULTS For murmurs detection, the in-person auscultation agreement between Stemoscope and the acoustic stethoscope was 91% (p = 0.67). The agreement between Stemoscope tele-auscultation and the acoustic stethoscope in-person auscultation was 90% (p = 0.32). When using the echocardiographic findings as the reference, the agreement between Stemoscope (tele-auscultation) and the acoustic stethoscope (in-person auscultation) was 89% vs. 86% (p = 1.00). The system evaluated by ten cardiologists is considered easy to use, and most of them would consider using it in a telemedical setting. CONCLUSION In-person auscultation and tele-auscultation by the Stemoscope are in good agreement with manual acoustic auscultation. The Stemoscope is a helpful heart murmur screening tool at a distance and can be used in telemedicine.
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Elias P, Poterucha TJ, Rajaram V, Moller LM, Rodriguez V, Bhave S, Hahn RT, Tison G, Abreau SA, Barrios J, Torres JN, Hughes JW, Perez MV, Finer J, Kodali S, Khalique O, Hamid N, Schwartz A, Homma S, Kumaraiah D, Cohen DJ, Maurer MS, Einstein AJ, Nazif T, Leon MB, Perotte AJ. Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease. J Am Coll Cardiol 2022; 80:613-626. [PMID: 35926935 DOI: 10.1016/j.jacc.2022.05.029] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/05/2022] [Accepted: 05/09/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Valvular heart disease is an important contributor to cardiovascular morbidity and mortality and remains underdiagnosed. Deep learning analysis of electrocardiography (ECG) may be useful in detecting aortic stenosis (AS), aortic regurgitation (AR), and mitral regurgitation (MR). OBJECTIVES This study aimed to develop ECG deep learning algorithms to identify moderate or severe AS, AR, and MR alone and in combination. METHODS A total of 77,163 patients undergoing ECG within 1 year before echocardiography from 2005-2021 were identified and split into train (n = 43,165), validation (n = 12,950), and test sets (n = 21,048; 7.8% with any of AS, AR, or MR). Model performance was assessed using area under the receiver-operating characteristic (AU-ROC) and precision-recall curves. Outside validation was conducted on an independent data set. Test accuracy was modeled using different disease prevalence levels to simulate screening efficacy using the deep learning model. RESULTS The deep learning algorithm model accuracy was as follows: AS (AU-ROC: 0.88), AR (AU-ROC: 0.77), MR (AU-ROC: 0.83), and any of AS, AR, or MR (AU-ROC: 0.84; sensitivity 78%, specificity 73%) with similar accuracy in external validation. In screening program modeling, test characteristics were dependent on underlying prevalence and selected sensitivity levels. At a prevalence of 7.8%, the positive and negative predictive values were 20% and 97.6%, respectively. CONCLUSIONS Deep learning analysis of the ECG can accurately detect AS, AR, and MR in this multicenter cohort and may serve as the basis for the development of a valvular heart disease screening program.
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Affiliation(s)
- Pierre Elias
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA
| | - Timothy J Poterucha
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA
| | - Vijay Rajaram
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA
| | - Luca Matos Moller
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA
| | - Victor Rodriguez
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Shreyas Bhave
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Rebecca T Hahn
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA
| | - Geoffrey Tison
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Sean A Abreau
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | - Joshua Barrios
- Division of Cardiology, University of California-San Francisco, San Francisco, California, USA
| | | | - J Weston Hughes
- Division of Cardiology, Stanford University, Palo Alto, California, USA
| | - Marco V Perez
- Division of Cardiology, Stanford University, Palo Alto, California, USA
| | - Joshua Finer
- NewYork-Presbyterian Hospital, New York, New York, USA
| | - Susheel Kodali
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA
| | - Omar Khalique
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA
| | - Nadira Hamid
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA
| | - Allan Schwartz
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA
| | - Shunichi Homma
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA
| | - Deepa Kumaraiah
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA
| | - David J Cohen
- Cardiovascular Research Foundation, New York, New York, USA; Department of Cardiology, St. Francis Hospital, Roslyn, New York, USA
| | - Mathew S Maurer
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA
| | - Andrew J Einstein
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA
| | - Tamim Nazif
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA
| | - Martin B Leon
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA; Cardiovascular Research Foundation, New York, New York, USA
| | - Adler J Perotte
- Department of Biomedical Informatics, Columbia University, New York, New York, USA.
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Gupta MD, Kunal S, Girish M, Gupta A, Yadav R. Artificial intelligence in Cardiology: the past, present and future. Indian Heart J 2022; 74:265-269. [PMID: 35917970 PMCID: PMC9453051 DOI: 10.1016/j.ihj.2022.07.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Berezin AE, Berezin AA. Point-of-care heart failure platform: where are we now and where are we going to? Expert Rev Cardiovasc Ther 2022; 20:419-429. [PMID: 35588730 DOI: 10.1080/14779072.2022.2080657] [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] [Indexed: 10/18/2022]
Abstract
INTRODUCTION Heart failure (HF) remains a leading cause of cardiovascular (CV) mortality in patients with CV disease. The point-of-care (POC) HF platform seems to be an ideal non-invasive workflow-adapted system for personally adjusted management of patients with HF. AREAS COVERED In the present manuscript, we reviewed the literature covering some relevant studies regarding the role of point-of care heart failure platform in the risk stratification, earlier diagnosis and prognostically beneficial treatment of patients with different phenotypes of HF. EXPERT OPINION POC HF platform including personal consultation, optimization of the comorbidity treatment, step-by-step HF diagnostic algorithm, single biomarker measurements, has also partially been provided in the current guidelines. Although there are several obstacles to implement POC in routine practice, such as education level, aging, affordability of health care, even partial implementation of POC can also improve clinical outcomes. POC seems to be an evolving model, more research studies are required to clearly see whether it helps to make better decisions with diagnosis and care of HF, as well helps to achieve better clinical outcomes.In summary, the POC HF platform is considered to be a more effective tool than conventional algorithm of HF management.
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Affiliation(s)
- Alexander E Berezin
- Internal Medicine Department, Zaporozhye State Medical University, 26, Mayakovsky av., Zaporozhye, Ukraine
| | - Alexander A Berezin
- Internal Medicine Department, Zaporozhye Medical Academy of Postgraduate Education, Zaporozhye, Ukraine
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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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Shen CP, Muse ED. Towards an artificial intelligence-augmented, ECG-enabled physical exam. Lancet Digit Health 2022; 4:e78-e79. [DOI: 10.1016/s2589-7500(21)00281-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 12/01/2021] [Indexed: 10/19/2022]
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