It is a common misperception that electrocardiograms (ECGs) simply contain data about heart activity. However, modern ECGs ...
The 12-lead ECG hasn't changed in a century. The algorithms reading it have. Three CEOs and one educator on whether doctors should trust the model ...
The company says its machine learning approach could help flag cardiac amyloidosis from standard 12-lead ECGs, though experts ...
Researchers developed a hybrid UMAP-HDBSCAN-SVM machine learning workflow to rapidly classify low-loss STEM-EELS spectrum ...
The use of AI in health care is challenging because sensitive patient data is scattered across different systems, and its use ...
Abstract: In this paper we present fully automatic interpatient electrocardiogram (ECG) signal classification method using deep convolutional neural networks (CNN). ECG is simple and non-invasive way ...
Abstract: Myocardial infarction (MI), commonly known as a heart attack, results from reduced blood flow to a part of the heart. Timely diagnosis of MI is very crucial due to its high mortality rate, ...
A machine learning model developed by researchers at the Johns Hopkins Kimmel Cancer Center filters out the biological noise in liquid biopsy samples, helping clinicians better match therapies to ...
Machine learning has emerged as a transformative force in the field of neurosurgery, offering innovative tools to predict surgical outcomes with greater ...
A multimodal deep learning framework trained on paired CT and MRI data demonstrated improved diagnostic accuracy when classifying patients with Alzheimer disease, mild cognitive impairment, or normal ...
Background: Federated learning (FL) is a newly proposed machine-learning method that uses a decentralized dataset. Since data transfer is not necessary for the learning process in FL, there is a ...
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