A deep learning model using transthoracic echocardiograms (TTEs) can accurately predict patients with active or occult atrial fibrillation (AF). The model was trained on…
Browsing: Electrocardiogram
This article discusses the challenge of extracting information from noisy environments in both classical and quantum computing. The authors propose a quantum smoothing filter…
This article looks at the top 20 countries in AI and the latest developments in the field. It also discusses a groundbreaking study presented…
This article presents a new Holter monitoring database from patients with paroxysmal atrial fibrillation (AF). The database consists of 167 records from 152 patients,…
Mount Sinai researchers have developed an innovative artificial intelligence (AI) model for electrocardiogram (ECG) analysis that allows for the interpretation of ECGs as language.…
Mount Sinai researchers have developed an AI model for ECG analysis that interprets ECGs as language, allowing for more accurate and effective diagnoses. The…
Mount Sinai researchers have developed an innovative artificial intelligence (AI) model for electrocardiogram (ECG) analysis that allows for the interpretation of ECGs as language.…
Mount Sinai researchers have developed an innovative artificial intelligence (AI) model for electrocardiogram (ECG) analysis that uses language to interpret ECGs. This model, known…
Mount Sinai researchers have developed an AI model for ECG analysis that interprets ECGs as language, allowing for more accurate and effective diagnoses. The…
Machine learning (ML) methods for the analysis of electrocardiography (ECG) data are gaining importance, and are supported by the release of large public datasets.…