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Machine Learning Model Discriminate Ischemic Heart Disease Using Breathome Analysis.
Biomedicines
December 2024
World-Class Research Center «Digital Biodesign and Personalized Healthcare», I.M. Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya Street, 119991 Moscow, Russia.
Ischemic heart disease (IHD) impacts the quality of life and is the most frequently reported cause of morbidity and mortality globally. To assess the changes in the exhaled volatile organic compounds (VOCs) in patients with vs. without ischemic heart disease (IHD) confirmed by stress computed tomography myocardial perfusion (CTP) imaging.
View Article and Find Full Text PDFMachine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms.
Diagnostics (Basel)
December 2024
Computer Science Department, Taif University, Taif 21944, Saudi Arabia.
: In the United States, chronic obstructive pulmonary disease (COPD) is a significant cause of mortality. As far as we know, it is a chronic, inflammatory lung condition that cuts off airflow to the lungs. Many symptoms have been reported for such a disease: breathing problems, coughing, wheezing, and mucus production.
View Article and Find Full Text PDFMachine perfusion organ preservation: Highlights from the American transplant congress 2024.
Artif Organs
January 2025
Department of Surgery, Division of Transplantation, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.
The American Transplant Congress (ATC) 2024, held in Philadelphia, serves as a vital platform for unveiling new research and clinical experience in organ machine perfusion-a key area in organ transplantation. This year's congress gathered 4652 participants from 49 countries, including top experts, to spotlight innovations in machine perfusion across various organ types, such as the liver, kidney, heart, and lung. A total of 87 abstracts on organ machine perfusion were presented.
View Article and Find Full Text PDFIntelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models.
Sci Rep
January 2025
Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, 11451, Riyadh, Saudi Arabia.
This study focuses on the use of machine learning (ML) models to predict the biodistribution of nanoparticles in various organs, using a dataset derived from research on nanoparticle behavior for cancer treatment. The dataset includes both categorical and numerical variables related to nanoparticle properties, with a focus on their distribution across organs such as the tumor, heart, liver, spleen, lung, and kidney tissues. In order to address the complex and non-linear nature of the data, three machine learning models were utilized: Bayesian Ridge Regression (BRR), Kernel Ridge Regression (KRR), and K-Nearest Neighbors (KNN).
View Article and Find Full Text PDFExplainable PET-Based Habitat and Peritumoral Machine Learning Model for Predicting Progression-free Survival in Clinical Stage IA Pure-Solid Non-small Cell Lung Cancer: A Two-center Study.
Acad Radiol
January 2025
Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Key Laboratory of Novel Nuclide Technologies on Precision Diagnosis and Treatment & Clinical Transformation of Wenzhou City, China (K.T.). Electronic address:
Article Synopsis
- The study focused on using machine learning and PET imaging to predict how long patients with early-stage non-small cell lung cancer (NSCLC) can expect to live without disease progression.
- Researchers analyzed data from 234 patients and created a model that combined radiomic features from tumor and surrounding areas with clinical data, resulting in a highly accurate predictive tool for patient outcomes.
- The findings indicated that certain imaging features can effectively distinguish between high-risk and low-risk patients, highlighting the importance of these radiomic signatures as independent markers for patient prognosis.
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