Arrhythmias

Enhancing Visual Inspection Capability of Multi-Modal Large Language Models on Medical Time Series with Supportive Conformalized and Interpretable Small Specialized Models

Large language models (LLMs) exhibit remarkable capabilities in visual inspection of medical time-series data, achieving proficiency comparable to human clinicians. However, their broad scope limits domain-specific precision, and proprietary weights hinder fine-tuning for specialized datasets. In contrast, small specialized models (SSMs) excel in targeted tasks but lack the contextual reasoning ...

DiffuSETS: 12-lead ECG Generation Conditioned on Clinical Text Reports and Patient-Specific Information

Heart disease remains a significant threat to human health. As a non-invasive diagnostic tool, the electrocardiogram (ECG) is one of the most widely used methods for cardiac screening. However, the scarcity of high-quality ECG data, driven by privacy concerns and limited medical resources, creates a pressing need for effective ECG signal generation. Existing approaches for generating ECG signals...

Optimization of connectome weights for a neural network model generating both forward and backward locomotion in C. elegans

Previous studies tracking the relationship between manipulations of C. elegans neurons and the resulting behavioral changes have called for the develo...

TCUP – An Open Access Tool to Predict Tissue of Origin and Cancer of Unknown Primary (CUP)

Cancer of unknown primary (CUP) remains a major diagnostic hurdle, compromising therapies that depend on accurately identifying tissue of origin. We p...

Atypical collective oscillatory activity in cardiac tissue uncovered by optogenetics

Many biological processes emerge as frequency-dependent responses to trains of external stimuli. Heart rhythm disturbances, i.e. cardiac arrhythmias, ...

Prediction of TdP Arrhythmia Risk Through Molecular Simulations of Conformation-specific Drug Interactions with the hERG K+, NaV1.5, and CaV1.2 Channels

Unintended block of cardiac ion channels, particularly hERG (KV11.1), remains a key concern in drug development as disruption of ion channel function ...

A multifaceted approach for obstructive sleep apnea classification from ECG signal using deep learning

Obstructive sleep apnea (OSA) is a common sleep disorder associated with increased cardiovascular and neurocognitive risks. While polysomnography rema...

Deep learning the dynamic regulatory sequence code of cardiac organoid differentiation

Defining the temporal gene regulatory programs that drive human organogenesis is essential for understanding the origins of congenital disease. We com...

AI-assisted modeling of attention quantifies engagement and predicts cognitive improvement in older adults

Cognitive training aims to prevent or slow cognitive decline in older adults, but outcomes vary widely. Engagement, describing how individuals allocat...

Heart rate fragmentation improves general anesthesia state classification using machine learning

Accurate assessment of consciousness during general anesthesia is crucial for optimizing anesthetic dosage and patient safety. Current electroencephal...

Automated Digital Biomarker Discovery Pipeline for Cardiovascular Diseases

Cardiovascular Diseases (CVDs) are the leading cause of mortality worldwide, necessitating early and accurate diagnosis to prevent severe outcomes suc...

Tracking the Preclinical Progression of Transthyretin Amyloid Cardiomyopathy Using Artificial Intelligence-Enabled Electrocardiography and Echocardiography

The diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) requires advanced imaging, precluding large-scale pre-clinical testing. Artificial int...

Genetic variants risk assessment for Long QT Syndrome through machine learning and multielectrode array recordings

Long QT syndrome (LQTS) is a life-threatening genetic disorder characterized by prolonged QT intervals on electrocardiograms. Congenital forms are mos...

Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals

Schizophrenia and bipolar disorder are severe mental illnesses that significantly impact quality of life. These disorders are associated with autonomi...

Foundation models for generalizable electrocardiogram interpretation: comparison of supervised and self-supervised electrocardiogram foundation models

The 12-lead electrocardiogram (ECG) remains a cornerstone of cardiac diagnostics, yet existing artificial intelligence (AI) solutions for automated in...

Profile of deaths mentioning ischemic and hemorrhagic stroke in Brazil: a population-based machine learning analysis

Brazil has the highest stroke rates in Latin America. The aim of this study was to investigate the profile of deaths mentioning stroke in Brazil betwe...

Interethnic Validation of Artificial Intelligence for prediction of Atrial Fibrillation Using Sinus Rhythm Electrocardiogram

Previous research has demonstrated acceptable diagnostic accuracy of AI-enabled sinus rhythm (SR) electrocardiogram (ECG) interpretation for predictin...

A Comparative Study of Predictive model (ECG Buddy) and ChatGPT-4o for Myocardial Infarction Diagnosis via ECG image Analysis: Performance, Accuracy, and Clinical Feasibility

Accurate and timely electrocardiogram (ECG) interpretation is critical for diagnosing myocardial infarction (MI) in emergency settings. Recent advance...

Integrating AI-ECG and Point-of-Care Cardiac Ultrasound for Screening Structural Heart Disease: A Proof-of-Concept Study

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

Artificial Intelligence-Based Automated Interpretation of Images of Electrocardiograms: Development and Multinational Validation of ECG-GPT

Timely and accurate assessment of electrocardiograms (ECGs) is crucial for diagnosing, triaging, and clinically managing patients. Current workflows r...