Myocardial Infarction

Low-complexity fetal heart rate monitoring from carbon-based single-channel dry electrodes maternal electrocardiogram.

Objective. Fetal and maternal health during pregnancy can be monitored with sensors such as Doppler or scalp fetal ECG. This study focuses on single-channel dry electrode maternal abdominal ECG (aECG) to extract fetal heart rate (fHR) using a low-complexity algorithm suitable for low-power wearables.Approach. A hybrid model combining machine learning, QRS masking, and data fusion was trained on tw...

[Artificial intelligence in risk stratification of acute coronary syndrome : Future vision or already reality?].

Artificial intelligence (AI) in cardiology has evolved from rule-based expert systems to data-driven, learning models that can support diagnostic and therapeutic decision-making in acute coronary syndrome (ACS). At their core, these are statistical prediction models that infer conditional probabilities for clinical events from data and are therefore, in principle, ideally suited for risk-based dec...

DeeBayes: An interpretable deep Bayesian network for ECG signal restoration.

The electrocardiogram (ECG) is a valuable and non-invasive tool for detecting and preventing arrhythmias. However, in real-world situations, ECG signa...

Old Criteria, New Intelligence: The Evolution of ECG in Pulmonary Hypertension Diagnosis.

BACKGROUND: Pulmonary hypertension (PH) carries a significant mortality risk, highlighting the need for improved early detection strategies. This revi...

External validation of an explainable electrocardiogram-only deep learning algorithm for the prediction of response after cardiac resynchronization therapy.

BACKGROUND: Cardiac resynchronization therapy (CRT) can improve clinical outcomes in patients with dyssynchronous heart failure, but many patients sel...

Multi-scale heart simulation augments the explainability of artificial intelligence-enabled electrocardiogram through provision of an electrocardiogram database labelled with cellular pathologies.

BACKGROUND AND OBJECTIVES: Although artificial-intelligence-enhanced electrocardiograms (AI-ECGs) offer prediction and diagnosis capabilities superior...

Predicting Occlusion Myocardial Infarctions in the Emergency Department Using Artificial Intelligence.

OBJECTIVES: The objective was to develop an artificial intelligence (AI) model for predicting acute coronary occlusion myocardial infarction (OMI) in ...

The Emerging Role of ECG-AI as a Digital Risk Biomarker for Incident Heart Failure.

Design and rationale of the WARRIOR ancillary study for coronary CT angiographic analysis.

BACKGROUND: Over 50% of women evaluated for suspected ischemia have no obstructive coronary artery disease (INOCA). Statins, angiotensin converting en...

ECG-aBcDe: Overcoming model dependence, encoding ECG into a universal language for any large language model.

Large Language Models (LLMs) hold significant promise for electrocardiogram (ECG) analysis, yet challenges remain regarding transferability, time-scal...

Coronary artery segmentation in non-contrast calcium scoring CT images using deep learning.

Precise localization of coronary arteries in Computed Tomography (CT) scans is critical from the perspective of medical assessment of various heart pa...

Inaccurate information regarding cardiovascular disease prevention enabled by generative artificial intelligence.

Inaccurate information regarding cardiovascular disease (CVD) prevention is prevalent on the internet and may influence medical decisions. Artificial ...

Integrating AI-ECG and point-of-care cardiac ultrasound for screening structural heart disease: A proof-of-concept study.

BACKGROUND: Early structural heart disease (SHD) detection is crucial for improving prognostic outcomes, but widely accessible screening methods are l...

AI-Enabled Early Detection of Chemo-Induced Cardiotoxicity Patterns Using ECG Time Series Data: A Simulated Oncology Framework.

OBJECTIVES: Chemotherapy-induced cardiotoxicity is still a major clinical problem, usually appearing subclinically before structural or symptomatic ca...

Advances in occult cancer screening for patients with unprovoked venous thromboembolism: A narrative review of epidemiology, risk, and clinical strategies.

BACKGROUND: Venous thromboembolism (VTE) and cancer exhibit a bidirectional correlation. The probability of detecting occult cancer in unprovoked VTE ...

Exploring Patients' Profiles Associated With the Resolution of Acute Calcium Pyrophosphate Arthritis Treated With Colchicine and Prednisone: Post Hoc Analysis of a Randomized Controlled Trial.

OBJECTIVE: The objective was to identify factors determining acute arthritis resolution and safety with colchicine and prednisone in acute calcium pyr...

Artificial Intelligence in detection of acute coronary occlusion in NSTEMI patients.

The prognosis of patients with MI has improved significantly with the recognition that early reperfusion is critical, particularly since timely percut...

Establishment and validation of an electrocardiogram vector-based machine learning model for the conversion of prone position electrocardiograms into standard electrocardiograms.

AIMS: The prone electrocardiogram (ECG) presents challenges in detecting anterior ST-segment elevated myocardial infarction (STEMI). This study aims t...

Quantitative CT of emphysema, wall thickness and mucus plugs in alpha-1-antitrypsin deficiency: relationship to clinical outcomes.

OBJECTIVES: Alpha-1-antitrypsin deficiency (AATD) is a rare genetic disorder leading to chronic obstructive pulmonary disease (COPD). Emphysema is the...

Artificial intelligence-enabled electrocardiographic sex discordance and the risk of incident atrial fibrillation: A multinational cohort study.

BACKGROUND: Binary classification of sex fails to capture the sex-related continuum of atrial fibrillation (AF) risk. OBJECTIVE: This study aimed to d...