Latest AI and machine learning research in arrhythmias for healthcare professionals.
Recent advances in deep learning and natural language generation have significantly improved image captioning, enabling automated, human-like descriptions for visual content. In this work, we apply these captioning techniques to generate clinician-like interpretations of ECG data. This study leverages existing ECG datasets accompanied by free-text reports authored by healthcare professionals (HC...
Background: Liver diseases present a significant global health challenge and often require costly, invasive diagnostics. Electrocardiography (ECG), a widely available and non-invasive tool, can enable the detection of liver disease by capturing cardiovascular-hepatic interactions. Methods: We trained tree-based machine learning models on ECG features to detect liver diseases using two large da...
Sudden Cardiac Arrest (SCA) is the leading cause of death among athletes of all age levels worldwide. Current prescreening methods for cardiac risk ...
Diffusion models dominate the space of text-to-image generation, yet they may produce undesirable outputs, including explicit content or private dat...
BACKGROUND: The PRAISE (PRedicting with Artificial Intelligence riSk aftEr acute coronary syndrome) score is a machine learning-based model for predic...
Artificial intelligence (AI)-enabled interpretation of electrocardiogram (ECG) images (AI-ECGs) can identify patterns predictive of future adverse car...
The real time analysis and secure transmission of electrocardiogram (ECG) signals are critical for ensuring both effective medical diagnosis and pat...
This study introduces the development of a state of the art, real time ECG monitoring and analysis system, incorporating cutting edge medical techno...
BACKGROUND: Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet...
OBJECTIVE: Maximizing safe resection in neuro-oncology has become paramount to improving patient survival and outcomes. Laser interstitial thermal the...
Atrial fibrillation (AF) is characterized by irregular electrical impulses originating in the atria, which can lead to severe complications and even...
Cardiovascular diseases are best diagnosed using multiple modalities that assess both the heart's electrical and mechanical functions. While effecti...
Objectives: To investigate clinicians' attitudes towards current automated interpretation of ECG and novel AI technologies and their perception of c...
This study presented a novel approach for the precise ablation of breast tumors using focused ultrasound (FUS), leveraging a physics-informed neural n...
Atrial fibrillation is a commonly encountered clinical arrhythmia associated with stroke and increased mortality. Since professional medical knowled...
We introduce the ECG-Image-Database, a large and diverse collection of electrocardiogram (ECG) images generated from ECG time-series data, with real...
Finite element method applications are a common approach to simulate a handful of phenomena but can take a lot of computing power, causing elevated ...
The Complex Emotion Recognition System (CERS) deciphers complex emotional states by examining combinations of basic emotions expressed, their interc...
Purpose To develop and evaluate a publicly available deep learning model for segmenting and classifying cardiac implantable electronic devices (CIEDs)...
The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical sig...