PURPOSE: Clinical validation of "BrainLossNet", a deep learning-based method for fast and robust estimation of brain volume loss (BVL) from longitudinal T1-weighted MRI, for the detection of accelerated BVL in multiple sclerosis (MS) and for the disc...
OBJECTIVEC: This is a cross-sectional and descriptive study to determine the levels of artificial intelligence anxiety and readiness of neonatal nurses.
Early diagnosis of cervicitis is important. Previous studies have found that neutrophil extracellular traps (NETs) play pro-inflammatory and anti-inflammatory roles in many diseases, suggesting that they may be involved in the inflammation of the ute...
AIM: To compare clinical outcomes using short and long co-incubation protocols in sibling oocytes based on embryo morphokinetic outcomes measured by time-lapse incubator with stratification based on a woman's age and sperm quality.
BACKGROUND AND AIMS: EUS is sensitive in detecting pancreatic neuroendocrine neoplasm (pNEN). However, the endoscopic diagnosis of pNEN is operator-dependent and time-consuming because pNEN mimics normal pancreas and other pancreatic lesions. We inte...
BACKGROUND: Machine learning algorithms are essential for predicting severe outcomes during public health crises like COVID-19. However, the dynamic nature of diseases requires continual evaluation and updating of these algorithms. This study aims to...
Respiration; international review of thoracic diseases
Oct 17, 2024
INTRODUCTION: Simulation-based training has proven effective for learning flexible bronchoscopy. However, no studies have tested the efficacy of training toward established proficiency criteria, i.e., mastery learning (ML). We wish to test the effect...
American journal of pharmaceutical education
Oct 17, 2024
OBJECTIVE: This study explored student pharmacists' perceptions and attitudes regarding artificial intelligence (AI) and machine learning (ML) in pharmacy practice. Due to AI/ML's promising prospects, understanding students' current awareness, compre...
OBJECTIVE: Investigation of explainable deep learning methods for graph neural networks to predict HIV infections with social network information and performing domain adaptation to evaluate model transferability across different datasets.
BACKGROUND: Distinguishing between unilateral and bilateral primary aldosteronism, a major cause of secondary hypertension, is crucial due to different treatment approaches. While adrenal venous sampling is the gold standard, its invasiveness, limite...
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