Artificial intelligence in cardiovascular medicine: clinical applications

TF Lüscher, FA Wenzl, F D'Ascenzo… - European heart …, 2024 - academic.oup.com
Clinical medicine requires the integration of various forms of patient data including
demographics, symptom characteristics, electrocardiogram findings, laboratory values …

Organomics: A concept reflecting the importance of PET/CT healthy organ radiomics in non-small cell lung cancer prognosis prediction using machine learning

Y Salimi, G Hajianfar, Z Mansouri, A Sanaat, M Amini… - medRxiv, 2024 - medrxiv.org
Purpose: Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer.
Patient survival prediction using machine learning and radiomics analysis proved to provide …

Debiasing Cardiac Imaging with Controlled Latent Diffusion Models

G Skorupko, R Osuala, Z Szafranowska… - arXiv preprint arXiv …, 2024 - arxiv.org
The progress in deep learning solutions for disease diagnosis and prognosis based on
cardiac magnetic resonance imaging is hindered by highly imbalanced and biased training …

MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study

S Hassan, D Akaila, M Arjemandi, V Papineni… - arXiv preprint arXiv …, 2024 - arxiv.org
In the complex realm of cognitive disorders, Alzheimer's disease (AD) and vascular
dementia (VaD) are the two most prevalent dementia types, presenting entangled symptoms …

[PDF][PDF] Relation of Pericardial Adipose Tissue Thickness by Echocardiography to Coronary Artery Disease

KW Hassan, OF Othman - University of Thi-Qar Journal Of Medicine, 2024 - jmed.utq.edu.iq
Relation of Pericardial Adipose Tissue Thickness by Echocardiography to Coronary Artery
Disease Page 1 Thi-Qar Medical Journal (TQMJ): Vol. (28), No. (2), 2024 Web Site: https://jmed.utq.edu …