Computational intelligence and neuroscience
May 9, 2022
Artificial intelligence is an emerging technology that revolutionizes human lives. Despite the fact that this technology is used in higher education, many professors are unaware of it. In this current scenario, there is a huge need to arise, implemen...
Computational intelligence and neuroscience
May 9, 2022
This study explores the memory characteristics of elderly individuals to design effective smart devices based on smart memory storage solutions under deep learning to improve the learning efficiency of elderly individuals. The different memory format...
OBJECTIVE: To analyze the critical alarms predictors of clinical deterioration/sepsis for clinical decision making in patients admitted to a reference hospital complex.
BACKGROUND: Deep learning segmentation requires large datasets with ground truth. Image annotation is time consuming and leads to shortages of ground truth data for clinical imaging. This study is to investigate the feasibility of kidney segmentation...
Humans are exposed to numerous compounds daily, some of which have adverse effects on health. Computational approaches for modeling toxicological data in conjunction with machine learning algorithms have gained popularity over the last few years. Mac...
BACKGROUND: The high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and evaluation...
OBJECTIVES: To develop an automatic segmentation algorithm using a deep neural network with transfer learning applicable to whole-body PET-CT images in children.
OBJECTIVES: Volumetric evaluation of coronary artery disease (CAD) allows better prediction of cardiac events. However, CAD segmentation is labor intensive. Our objective was to create an open-source deep learning (DL) model to segment coronary plaqu...
Journal of cardiothoracic and vascular anesthesia
May 7, 2022
OBJECTIVES: Identifying patients with low left ventricular ejection fraction (LVEF) and monitoring LVEF responses to treatment are important clinical goals. Can a deep-learning algorithm predict pediatric LVEF within clinically acceptable error?
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.