It is a common misperception that electrocardiograms (ECGs) simply contain data about heart activity. However, modern ECGs ...
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 ...
A new study published in Engineering has combined machine learning (ML) and experimental validation to identify dihydromyricetin (DHM), a natural flavonoid, as a potent inhibitor of the TGF-β/ALK5 ...
The use of AI in health care is challenging because sensitive patient data is scattered across different systems, and its use ...
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, ...
Abstract: The aim of this study is to carry out a comparative study of the accuracy performance of the Random Forest and Logistic Regression model in the task of classifying the Heart Axis, which is a ...
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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 ...
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