Publications by authors named "Antonio H Ribeiro"

Article Synopsis
  • Cardiac trabeculae are muscular structures in the heart that have a crucial but not fully understood role in heart function and disease.
  • A study involving over 47,000 participants from the UK Biobank found links between trabecular shape and rare genetic variants in 56 genes related to heart muscle function and development.
  • The research also revealed 68 genetic regions associated with heart condition pathways, indicating that variations in trabeculation may influence the severity of heart diseases like hypertrophic and dilated cardiomyopathy.
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Article Synopsis
  • The study explores the use of advanced neural network-derived ECG features to predict cardiovascular disease and mortality, aiming to uncover subtle, important indicators that traditional methods might miss.
  • Using data from over 1.8 million patients and various international cohorts, researchers identified three distinct phenogroups, with one, phenogroup B, showing a significantly higher mortality risk—20% more than phenogroup A.
  • The findings suggest that neural network ECG features not only indicate future health risks like atrial fibrillation and ischemic heart disease but also highlight specific genetic loci that may contribute to these risks.
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Article Synopsis
  • - The AI-ECG risk estimator (AIRE) platform was developed to improve predictions of future disease and mortality risks from electrocardiograms (ECGs), addressing limitations in existing models related to individual actionability and biological plausibility.
  • - AIRE utilizes deep learning and survival analysis on a massive dataset of over 1.16 million ECGs to predict patient-specific mortality risks and timelines, validated across diverse international cohorts.
  • - The platform demonstrated high accuracy for predicting various health risks, such as all-cause mortality and heart failure, and identified biological pathways linked to cardiac health, making it a promising tool for clinical use globally.
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Deep learning's widespread use prompts heightened scrutiny, particularly in the biomedical fields, with a specific focus on model generalizability. This study delves into the influence of training data characteristics on the generalization performance of models, specifically in cardiac abnormality detection. Leveraging diverse electrocardiogram datasets, models are trained on subsets with varying characteristics and subsequently compared for performance.

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Imbalances in electrolyte concentrations can have severe consequences, but accurate and accessible measurements could improve patient outcomes. The current measurement method based on blood tests is accurate but invasive and time-consuming and is often unavailable for example in remote locations or an ambulance setting. In this paper, we explore the use of deep neural networks (DNNs) for regression tasks to accurately predict continuous electrolyte concentrations from electrocardiograms (ECGs), a quick and widely adopted tool.

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The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone.

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Aims: Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence (AI) to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. We hypothesize that AI models with a specific design can provide fine-grained interpretation of ECGs to advance cardiovascular diagnosis, stratify mortality risks, and identify new clinically useful information.

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Aims: Deep neural network artificial intelligence (DNN-AI)-based Heart Age estimations have been presented and used to show that the difference between an electrocardiogram (ECG)-estimated Heart Age and chronological age is associated with prognosis. An accurate ECG Heart Age, without DNNs, has been developed using explainable advanced ECG (A-ECG) methods. We aimed to evaluate the prognostic value of the explainable A-ECG Heart Age and compare its performance to a DNN-AI Heart Age.

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Background: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of cardiovas- cular diseases such as stroke and heart failure. Machine learning methods have shown promising results in evaluating the risk of developing atrial fibrillation from the electrocardiogram. We aim to develop and evaluate one such algorithm on a large CODE dataset collected in Brazil.

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Article Synopsis
  • Left ventricular (LV) systolic dysfunction significantly increases the risk of heart failure and premature death, highlighting the need for effective screening methods.
  • Researchers developed a deep learning algorithm that analyzes ECG images to detect LV systolic dysfunction and validated its accuracy using data from multiple hospitals and cohorts.
  • The model showed strong performance, achieving high accuracy metrics (AUROC values above 0.90) across different clinical settings, demonstrating its potential as a reliable screening tool for heart dysfunction.
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Background: Worldwide, it is estimated that over 6 million people are infected with Chagas disease (ChD). It is a neglected disease that can lead to severe heart conditions in its chronic phase. While early treatment can avoid complications, the early-stage detection rate is low.

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Article Synopsis
  • Deep neural networks were used to analyze ECGs to estimate ECG-age, which predicts health outcomes, and researchers examined its relevance in a long-term study involving FHS participants.
  • The study found that a gap between chronological age and ECG-age (Δage) significantly correlated with increased risks of death and various cardiovascular issues over an average follow-up of 17 years.
  • Specifically, every 10-year increase in Δage resulted in higher risks of all-cause mortality, atrial fibrillation, myocardial infarction, and heart failure, indicating that both accelerated and decelerated aging can impact health outcomes significantly.
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Background: People age at different rates. Biological age is a risk factor for many chronic diseases independent of chronological age. A good lifestyle is known to improve overall health, but its association with biological age is unclear.

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Objective: Over the past few years, deep learning (DL) has been used extensively in research for 12-lead electrocardiogram (ECG) analysis. However, it is unclear whether the explicit or implicit claims made on DL superiority to the more classical feature engineering (FE) approaches, based on domain knowledge, hold. In addition, it remains unclear whether combining DL with FE may improve performance over a single modality.

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Myocardial infarction diagnosis is a common challenge in the emergency department. In managed settings, deep learning-based models and especially convolutional deep models have shown promise in electrocardiogram (ECG) classification, but there is a lack of high-performing models for the diagnosis of myocardial infarction in real-world scenarios. We aimed to train and validate a deep learning model using ECGs to predict myocardial infarction in real-world emergency department patients.

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The application of artificial intelligence (AI) for automated diagnosis of electrocardiograms (ECGs) can improve care in remote settings but is limited by the reliance on infrequently available signal-based data. We report the development of a multilabel automated diagnosis model for electrocardiographic images, more suitable for broader use. A total of 2,228,236 12-lead ECGs signals from 811 municipalities in Brazil are transformed to ECG images in varying lead conformations to train a convolutional neural network (CNN) identifying 6 physician-defined clinical labels spanning rhythm and conduction disorders, and a hidden label for gender.

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The electrocardiogram (ECG) is the most commonly used exam for the evaluation of cardiovascular diseases. Here we propose that the age predicted by artificial intelligence (AI) from the raw ECG (ECG-age) can be a measure of cardiovascular health. A deep neural network is trained to predict a patient's age from the 12-lead ECG in the CODE study cohort (n = 1,558,415 patients).

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Aims: This study aims to assess whether information derived from the raw 12-lead electrocardiogram (ECG) combined with clinical information is predictive of atrial fibrillation (AF) development.

Methods And Results: We use a subset of the Telehealth Network of Minas Gerais (TNMG) database consisting of patients that had repeated 12-lead ECG measurements between 2010 and 2017 that is 1 130 404 recordings from 415 389 unique patients. Median and interquartile of age for the recordings were 58 (46-69) and 38% of the patients were males.

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Aims: Atrial fibrillation (AF) is a public health problem and its prevalence is increasing worldwide. Electronic cohorts, with large electrocardiogram (ECG) databases linked to mortality data, can be useful in determining prognostic value of ECG abnormalities. Our aim is to evaluate the risk of mortality in patients with AF from Brazil.

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The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice.

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SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.

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Background: Left bundle branch block is recognized as a marker of higher risk of death, but the prognostic value of the right bundle branch block in the general population is still controversial. Our aim is to evaluate the risk of overall and cardiovascular mortality in patients with right (RBBB) and left bundle branch block (LBBB) in a large electronic cohort of Brazilian patients.

Methods: This observational retrospective study was developed with the database of digital ECGs from Telehealth Network of Minas Gerais, Brazil (TNMG).

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