☆ 4.3 Review

Artificial intelligence and cardiology: Current status and perspective

JOURNAL OF CARDIOLOGY (2022)

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Review Cardiac & Cardiovascular Systems

Machine Learning and the Future of Cardiovascular Care JACC State-of-the-Art Review

Giorgio Quer et al.

Summary: Machine learning algorithms have the potential to support clinical experts in identifying diagnostic patterns and guiding treatment, but clinicians must take an active role in implementing these algorithms in practice. Additionally, clinicians need to understand key concepts in machine learning in order to better evaluate relevant literature and developments.

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY (2021)

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Article Cardiac & Cardiovascular Systems

Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality

Sheojung Shin et al.

Summary: The study aimed to compare the performance of machine learning methods with conventional statistical models for predicting readmission and mortality in heart failure patients. The results showed that machine learning algorithms had better discrimination in most studies, highlighting the need for external validation and evaluation using clinical quality standards for prognosis research in studies utilizing machine learning.

ESC HEART FAILURE (2021)

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Article Cardiac & Cardiovascular Systems

Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device

John R. Giudicessi et al.

Summary: An artificial intelligence algorithm was developed to accurately predict QTc values from a standard 12-lead ECG, which was then tested on a mobile ECG device. The study demonstrated the algorithm's excellent performance in detecting and predicting QTc values, potentially offering a cost-effective method for screening acquired and congenital long QT syndrome in clinical settings.

CIRCULATION (2021)

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Article Cardiac & Cardiovascular Systems

Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke

Sushravya Raghunath et al.

Summary: Deep learning can predict new-onset atrial fibrillation from the 12-lead ECG in patients without a previous history of AF, thereby helping to identify individuals at risk for AF-related strokes.

CIRCULATION (2021)

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Review Cardiac & Cardiovascular Systems

Machine Learning in Arrhythmia and Electrophysiology

Natalia A. Trayanova et al.

Summary: Machine learning, particularly in the field of cardiac electrophysiology and arrhythmias, has broad applications beyond just the automatic interpretation of ECGs. This review comprehensively examines various ML applications in this area, from general principles and methodologies to specific studies utilizing ML. Challenges and perspectives for ML-driven cardiac electrophysiology and arrhythmia research are also discussed in detail.

CIRCULATION RESEARCH (2021)

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Review Cardiac & Cardiovascular Systems

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management

Konstantinos C. Siontis et al.

Summary: This review summarizes the use of artificial intelligence-enhanced electrocardiography in the detection of cardiovascular disease in at-risk populations, discussing its implications for clinical decision-making in patients with cardiovascular disease and critically appraising potential limitations and unknowns.

NATURE REVIEWS CARDIOLOGY (2021)

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Review Cardiac & Cardiovascular Systems

Applications of artificial intelligence in cardiovascular imaging

Maxime Sermesant et al.

Summary: Research on artificial intelligence in cardiovascular imaging has made progress in addressing clinical questions and developing different approaches, including data-driven and models integrating anatomical and physiological information. The limitations of AI in cardiovascular imaging, such as generalizability and explainability, are also discussed along with potential solutions.

NATURE REVIEWS CARDIOLOGY (2021)

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Article Multidisciplinary Sciences

Transfer learning for ECG classification

Kuba Weimann et al.

Summary: Remote monitoring devices enable more effective healthcare for patients with periodic heart arrhythmia, but there is a need to develop reliable automatic ECG interpretation methods. This study uses deep learning to classify raw ECG recordings and shows that transfer learning improves classification performance effectively.

SCIENTIFIC REPORTS (2021)

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Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram

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JAMA CARDIOLOGY (2021)

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Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial

Xiaoxi Yao et al.

Summary: The study aimed to evaluate whether an AI-powered clinical decision support tool based on ECGs enables early diagnosis of low EF. Results showed that the intervention increased the diagnosis of low EF in primary care settings and among patients with a high likelihood of low EF, indicating the potential of using AI algorithms for early diagnosis in routine care.

NATURE MEDICINE (2021)

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Article Cardiac & Cardiovascular Systems

Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform

John S. Chorba et al.

Summary: This research utilizes a deep learning algorithm to detect murmurs and valvular heart disease on a commercial digital stethoscope platform, showing comparable performance to expert cardiologists. The algorithm's sensitivity and specificity vary in different contexts, indicating wide application potential.

JOURNAL OF THE AMERICAN HEART ASSOCIATION (2021)

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Article Cardiac & Cardiovascular Systems

Toward Replacing Late Gadolinium Enhancement With Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy

Qiang Zhang et al.

Summary: The study introduced a new CMR technology called VNE that can achieve LGE-like imaging without the need for contrast agent. VNE demonstrated high agreement with LGE in lesion distribution and quantification, while also providing significantly better image quality.

CIRCULATION (2021)

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Article Biology

Interpretable heartbeat classification using local model-agnostic explanations on ECGs

Ines Neves et al.

Summary: This study focuses on an Explainable Artificial Intelligence (XAI) solution to improve the interpretability of heartbeat classification. By introducing a novel method that adds temporal dependency between time samples in time series data, the classification explanation becomes more reliable and trustworthy.

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AI-Assisted Echocardiographic Prescreening of Heart Failure With Preserved Ejection Fraction on the Basis of Intrabeat Dynamics

Yu-An Chiou et al.

Summary: This study aimed to establish a rapid prescreening tool for heart failure with preserved ejection fraction (HFpEF) using artificial intelligence techniques to detect abnormal echocardiographic patterns in structure and function. The AI model showed high accuracy, sensitivity, and specificity in detecting HFpEF. It also provided valuable quantitative metrics to assist clinicians in the diagnosis of HFpEF.

JACC-CARDIOVASCULAR IMAGING (2021)

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Article Cardiac & Cardiovascular Systems

Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography

Ivar M. Salte et al.

Summary: This study demonstrated the feasibility and comparability of fully automated measurements of global longitudinal strain using a novel motion estimation technology based on deep learning and artificial intelligence. The AI method successfully identified standard apical views, timed cardiac events, traced the myocardium, estimated motion, and measured GLS across different cardiac pathologies and LV function levels. Automated measurements based on AI could streamline the evaluation of left ventricular function and improve clinical diagnostic efficiency.

JACC-CARDIOVASCULAR IMAGING (2021)

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Article Cardiac & Cardiovascular Systems

Machine Learning of ECG Waveforms to Improve Selection for Testing for Asymptomatic Left Ventricular Dysfunction

Elizabeth L. Potter et al.

Summary: Machine learning applied to ewECG is a sensitive screening test for LVD and its integration into screening of patients at risk for heart failure would reduce the number of echocardiograms by almost one-half.

JACC-CARDIOVASCULAR IMAGING (2021)

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Article Cardiac & Cardiovascular Systems

AI Filter Improves Positive Predictive Value of Atrial Fibrillation Detection by an Implantable Loop Recorder

Suneet Mittal et al.

Summary: The study aimed to evaluate the impact of using a 2-part AI filter on the PPV of ILR-detected AF events. Results showed a significant increase in PPV after employing the DNN filter, with the greatest improvement seen in AF episodes lasting over 30 minutes.

JACC-CLINICAL ELECTROPHYSIOLOGY (2021)

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Article Computer Science, Artificial Intelligence

Who Will Pay for AI?

Melissa M. Chen et al.

Summary: In 2020, the Centers for Medicare & Medicaid Services (CMS) in the United States established payment for artificial intelligence (AI) through two different systems – the Medicare Physician Fee Schedule (MPFS) and the Inpatient Prospective Payment System (IPPS). This included reimbursement for AI tools for diabetic retinopathy diagnosis and large-vessel occlusion stroke diagnosis and treatment. The article also proposes future payment pathways for AI technologies.

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Article Cardiac & Cardiovascular Systems

Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG

Giorgio Luongo et al.

Summary: Machine learning-based classification of 12-lead ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI.

CARDIOVASCULAR DIGITAL HEALTH JOURNAL (2021)

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Article Cardiac & Cardiovascular Systems

2021 ISHNE/HRS/EHRA/APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

Niraj Varma et al.

Summary: This collaborative statement discusses the current status of mobile health technologies in arrhythmia management, including the range of digital medical tools, clinical decision support, comorbidity and lifestyle management, patient self-management, as well as the promises of predictive analytics and operational challenges in embedding mHealth into routine clinical care.

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Diagnostic performance of an artificial intelligence-driven cardiac-structured reporting system for myocardial perfusion SPECT imaging

Ernest V. Garcia et al.

JOURNAL OF NUCLEAR CARDIOLOGY (2020)

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A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images

Kenya Kusunose et al.

JACC-CARDIOVASCULAR IMAGING (2020)

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Editorial Material Cardiac & Cardiovascular Systems

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Chayakrit Krittanawong et al.

NATURE REVIEWS CARDIOLOGY (2020)

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Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: the SEMMELWEIS-CRT score

Marton Tokodi et al.

EUROPEAN HEART JOURNAL (2020)

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Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram

Wei-Yin Ko et al.

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY (2020)

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Article Engineering, Biomedical

Sequential Factorized Autoencoder for Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms

Prashnna Kumar Gyawali et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2020)

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Automatic diagnosis of the 12-lead ECG using a deep neural network

Antonio H. Ribeiro et al.

NATURE COMMUNICATIONS (2020)

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Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness

Sebastian Vollmer et al.

BMJ-BRITISH MEDICAL JOURNAL (2020)

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Francisco Lopez-Jimenez et al.

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Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence Following Pulmonary Vein Isolation

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Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation A Population-Based Study

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Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study

Hongling Zhu et al.

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Rahul G. Muthalaly et al.

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Smartwatch Performance for the Detection and Quantification of Atrial Fibrillation

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Machine Learning Prediction of Response to Cardiac Resynchronization Therapy Improvement Versus Current Guidelines

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Zachi Attia et al.

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Maja Cikes et al.

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A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data

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Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes Lessons From the COMPANION Trial

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Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT A Multicenter Study

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Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA

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Analysis of Machine Learning Techniques for Heart Failure Readmissions

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