Machine learning in cardiology: Clinical application and basic research

Article Cardiac & Cardiovascular Systems

Deep learning to detect significant coronary artery disease from plain chest radiographs AI4CAD

Giuseppe D'Ancona et al.

Summary: A deep learning solution using chest radiographs can be used to detect significant coronary artery disease, and it has potential as a predictive tool.

INTERNATIONAL JOURNAL OF CARDIOLOGY (2023)

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Review Genetics & Heredity

Obtaining genetics insights from deep learning via explainable artificial intelligence

Gherman Novakovsky et al.

Summary: This Review describes the advances in explainable artificial intelligence (xAI) in genomics, where researchers are using deep learning approaches to gain biological insights behind the models, moving beyond the traditional 'black box' nature.

NATURE REVIEWS GENETICS (2023)

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Article Genetics & Heredity

Deep learning enables genetic analysis of the human thoracic aorta

James P. Pirruccello et al.

Summary: Genome-wide association analyses identified variants associated with thoracic aortic diameter and polygenic score for ascending aortic diameter was correlated with a diagnosis of thoracic aortic aneurysm. Enlargement or aneurysm of the aorta predisposes to dissection, and a deep learning model along with genome-wide association studies successfully identified loci associated with ascending and descending thoracic aortic diameter, highlighting the potential for rapidly defining quantitative traits with deep learning in biomedical images.

NATURE GENETICS (2022)

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Review Cell Biology

A guide to machine learning for biologists

Joe G. Greener et al.

Summary: This passage discusses the application of machine learning in the analysis of biological data and provides guidance for experimentalists. The increasing scale and complexity of biological data have led to a growing use of machine learning in biology.

NATURE REVIEWS MOLECULAR CELL BIOLOGY (2022)

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Review Cardiac & Cardiovascular Systems

Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care

Saeed Amal et al.

Summary: Today's digital health revolution aims to improve the efficiency of healthcare delivery and make care more personalized and timely. Data fusion techniques enable the integration of multiple modalities, providing deeper insights specific to the field of cardiovascular medicine and advancing the diagnosis and treatment of cardiovascular diseases.

FRONTIERS IN CARDIOVASCULAR MEDICINE (2022)

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Article Multidisciplinary Sciences

devCellPy is a machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data

Francisco X. Galdos et al.

Summary: devCellPy is a highly accurate and precise machine learning-enabled tool for automated prediction of cell types across complex cellular hierarchies. It has been demonstrated to be useful in constructing a murine cardiac developmental atlas and predicting cardiomyocyte subtypes in human induced pluripotent stem cells.

NATURE COMMUNICATIONS (2022)

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Article Biochemistry & Molecular Biology

Development of non-bias phenotypic drug screening for cardiomyocyte hypertrophy by image segmentation using deep learning

Jin Komuro et al.

Summary: The number of patients with heart failure and related deaths is increasing worldwide, posing a major problem. This study uses a deep learning-based high-throughput screening system to evaluate cardiomyocyte hypertrophy and identify drugs that inhibit it. The results show that ezetimibe is an effective inhibitor of cardiomyocyte hypertrophy. These findings suggest that deep learning technology has potential applications in evaluating cardiomyocyte hypertrophy and drug screening, contributing to the development of new treatments for heart failure.

BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS (2022)

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Article Multidisciplinary Sciences

Anti-senescent drug screening by deep learning-based morphology senescence scoring

Dai Kusumoto et al.

Summary: The paper presents a morphology-based deep learning system to identify senescent cells and evaluate the effects of anti-senescent reagents. The system uses convolutional neural networks (CNN) to classify cellular senescence and successfully identifies anti-senescent drugs through morphology-based drug screening.

NATURE COMMUNICATIONS (2021)

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Article Biochemical Research Methods

Effective gene expression prediction from sequence by integrating long-range interactions

Ziga Avsec et al.

Summary: Enformer leverages a new deep learning architecture to improve gene expression prediction accuracy based on DNA sequences, integrating information from long-range interactions in the genome and accurately predicting the impact of genetic variants on gene expression. Additionally, Enformer has learned to predict enhancer-promoter interactions directly from DNA sequences.

NATURE METHODS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Using Deep Learning to Emulate the Use of an External Contrast Agent in Cardiovascular 4D Flow MRI

Mariana Bustamante et al.

Summary: This study developed and evaluated a deep learning architecture to enhance blood-tissue contrast in noncontrast 4D flow MRI by emulating the use of an external contrast agent. The results showed significant improvements in contrast, CNR, SNR, and segmentation accuracy with the neural network enhanced images. The proposed approach could potentially improve data quality, visualization, and postprocessing of cardiovascular 4D flow data.

JOURNAL OF MAGNETIC RESONANCE IMAGING (2021)

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Article Biochemical Research Methods

Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning

Zhaoqiang Wang et al.

Summary: Combining a view-channel-depth neural network with light-field microscopy allows for artifact-free three-dimensional image sequences with uniform spatial resolution and high reconstruction throughput, enabling imaging of dynamic spatiotemporal patterns in samples at high speeds.

NATURE METHODS (2021)

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Article Biochemistry & Molecular Biology

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Rohan Gupta et al.

Summary: Drug designing and development faces challenges in terms of low efficacy, high cost, and complexity of data. However, artificial intelligence and machine learning technologies play a crucial role in overcoming these challenges and advancing the field.

MOLECULAR DIVERSITY (2021)

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Article Biochemistry & Molecular Biology

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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Editorial Material Biochemistry & Molecular Biology

Deep learning for detecting congenital heart disease in the fetus

Shaine A. Morris et al.

Summary: New developments in machine learning show promise in improving prenatal diagnosis of congenital health diseases and reducing disparities in this area.

NATURE MEDICINE (2021)

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Editorial Material Cardiac & Cardiovascular Systems

Bad News, You're Older Than Suspected Can Deep Learning Estimate Biological Age From Chest Radiographs?

Manish Motwani

JACC-CARDIOVASCULAR IMAGING (2021)

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Article Biochemical Research Methods

Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes

Jiji Chen et al.

Summary: In this study, we demonstrate the application of residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse fluorescence microscopy data, achieving competitive denoising results compared to other state-of-the-art neural networks. The RCAN network was also shown to enable resolution enhancement equivalent to, or better than, other networks through simulations and practical applications. Additionally, we explored the use of RCAN for denoising and resolution improvement in confocal microscopy, as well as in structured illumination microscopy, achieving substantial enhancements in lateral and axial resolution. The study also provides insights into the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluating and further enhancing network performance.

NATURE METHODS (2021)

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Article Multidisciplinary Sciences

Deep learning-based transformation of H&E stained tissues into special stains

Kevin de Haan et al.

Summary: A method for digitally transforming H&E stained tissue into special stains was introduced, showing improved diagnosis over using H&E only. Computational stain transformation from H&E to special stains demonstrated utility in improving diagnoses of non-neoplastic kidney diseases, providing time and cost savings.

NATURE COMMUNICATIONS (2021)

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Article Health Care Sciences & Services

Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care

Arne Peine et al.

Summary: The VentAI reinforcement learning algorithm provides reproducible high performance by dynamically suggesting an optimized, individualized ventilation strategy for critically ill patients. Compared to standard clinical care by physicians, VentAI significantly increased performance returns and recommended actions more frequently with specific settings for PEEP, FiO(2), and tidal volume. VentAI's preference for lower tidal volumes, certain PEEP levels, and avoiding high FiO(2) values could potentially benefit critically ill patients.

NPJ DIGITAL MEDICINE (2021)

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Review Radiology, Nuclear Medicine & Medical Imaging

Deep Learning for Quantitative Cardiac MRI

Qian Tao et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2020)

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Article Cardiac & Cardiovascular Systems

Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography

Joon-Myoung Kwon et al.

EUROPACE (2020)

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Article Cardiac & Cardiovascular Systems

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 Cardiac & Cardiovascular Systems

Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks

Rutger R. van de Leur et al.

JOURNAL OF THE AMERICAN HEART ASSOCIATION (2020)

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Article Biochemistry & Molecular Biology

Deep learning models for electrocardiograms are susceptible to adversarial attack

Xintian Han et al.

NATURE MEDICINE (2020)

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Article Biochemistry & Molecular Biology

Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network

Sushravya Raghunath et al.

NATURE MEDICINE (2020)

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Article Multidisciplinary Sciences

A promising approach for screening pulmonary hypertension based on frontal chest radiographs using deep learning: A retrospective study

Xiao-Ling Zou et al.

PLOS ONE (2020)

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Article Multidisciplinary Sciences

Direct evidence of secondary reconnection inside filamentary currents of magnetic flux ropes during magnetic reconnection

Salah Al-Zaiti et al.

NATURE COMMUNICATIONS (2020)

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Article Biochemistry & Molecular Biology

A population-based phenome-wide association study of cardiac and aortic structure and function

Wenjia Bai et al.

NATURE MEDICINE (2020)

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Review Biochemical Research Methods

Machine learning and statistical methods for clustering single-cell RNA-sequencing data

Raphael Petegrosso et al.

BRIEFINGS IN BIOINFORMATICS (2020)

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Article Cardiac & Cardiovascular Systems

Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram

Conner D. Galloway et al.

JAMA CARDIOLOGY (2019)

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Article Medicine, General & Internal

An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction

Zachi Attia et al.

LANCET (2019)

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Article Biochemistry & Molecular Biology

Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram

Zachi I. Attia et al.

NATURE MEDICINE (2019)

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Article Biochemistry & Molecular Biology

Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

Awni Y. Hannun et al.

NATURE MEDICINE (2019)

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Review Genetics & Heredity

Challenges in unsupervised clustering of single-cell RNA-seq data

Vladimir Yu Kiselev et al.

NATURE REVIEWS GENETICS (2019)

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Editorial Material Radiology, Nuclear Medicine & Medical Imaging

Deep Learning for Cardiac MRI: The Time Has Come

Patrick M. Colletti

RADIOLOGY (2019)

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Review Immunology

The application of convolutional neural network to stem cell biology

Dai Kusumoto et al.

INFLAMMATION AND REGENERATION (2019)

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Article Medicine, General & Internal

Deep Learning to Assess Long-term Mortality From Chest Radiographs

Michael T. Lu et al.

JAMA NETWORK OPEN (2019)

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Article Medical Informatics

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

Sara Bersche Golas et al.

BMC MEDICAL INFORMATICS AND DECISION MAKING (2018)

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Article Computer Science, Interdisciplinary Applications

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Olivier Bernard et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2018)

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Review Cardiac & Cardiovascular Systems

Artificial Intelligence in Cardiology

Kipp W. Johnson et al.

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY (2018)

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Letter Cardiac & Cardiovascular Systems

Artificial Intelligence-Based Assessment of Left Ventricular Filling Pressures From 2-Dimensional Cardiac Ultrasound Images

Alaa Mabrouk Salem Omar et al.

JACC-CARDIOVASCULAR IMAGING (2018)

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Article Cell & Tissue Engineering

Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells

Dai Kusumoto et al.

STEM CELL REPORTS (2018)

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Article Computer Science, Information Systems

Using recurrent neural network models for early detection of heart failure onset

Edward Choi et al.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2017)

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Article Multidisciplinary Sciences

Mastering the game of Go without human knowledge

David Silver et al.

NATURE (2017)

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Article Multidisciplinary Sciences

Dermatologist-level classification of skin cancer with deep neural networks

Andre Esteva et al.

NATURE (2017)

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Article Cardiac & Cardiovascular Systems

Cardiovascular Event Prediction by Machine Learning The Multi-Ethnic Study of Atherosclerosis

Bharath Ambale-Venkatesh et al.

CIRCULATION RESEARCH (2017)

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Article Cardiac & Cardiovascular Systems

Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography

Sukrit Narula et al.

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY (2016)

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Article Multidisciplinary Sciences

Mastering the game of Go with deep neural networks and tree search

David Silver et al.

NATURE (2016)

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Article Cardiac & Cardiovascular Systems

Evaluation of a Prediction Model for the Development of Atrial Fibrillation in a Repository of Electronic Medical Records

Matthew J. Kolek et al.

JAMA CARDIOLOGY (2016)

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Article Cardiac & Cardiovascular Systems

Machine Learning in Medicine

Rahul C. Deo

CIRCULATION (2015)

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Article Computer Science, Artificial Intelligence

Right ventricle segmentation from cardiac MRI: A collation study

Caroline Petitjean et al.

MEDICAL IMAGE ANALYSIS (2015)

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Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

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Article Biotechnology & Applied Microbiology

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

Babak Alipanahi et al.

NATURE BIOTECHNOLOGY (2015)

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Review Genetics & Heredity

Machine learning applications in genetics and genomics

Maxwell W. Libbrecht et al.

NATURE REVIEWS GENETICS (2015)

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Review Computer Science, Artificial Intelligence

Deep learning in neural networks: An overview

Juergen Schmidhuber

NEURAL NETWORKS (2015)

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Article Cell Biology

Identification of type 2 diabetes subgroups through topological analysis of patient similarity

Li Li et al.

SCIENCE TRANSLATIONAL MEDICINE (2015)

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Article Biochemical Research Methods

Unsupervised pattern discovery in human chromatin structure through genomic segmentation

Michael M. Hoffman et al.

NATURE METHODS (2012)

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Editorial Material Biochemical Research Methods

Illuminating eukaryotic transcription start sites

John A. Stamatoyannopoulos

NATURE METHODS (2010)

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Article Biochemical Research Methods

mRNA-Seq whole-transcriptome analysis of a single cell

Fuchou Tang et al.

NATURE METHODS (2009)

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Article Biochemistry & Molecular Biology

Predicting gene expression from sequence

MA Beer et al.

CELL (2004)

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Review Surgery

Artificial intelligence in medicine

AN Ramesh et al.

ANNALS OF THE ROYAL COLLEGE OF SURGEONS OF ENGLAND (2004)

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Machine learning in cardiology: Clinical application and basic research

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