BACKGROUND: In recent years, machine learning (ML)-based models have been widely used in clinical domains to predict clinical risk events. However, in production, the performances of such models heavily rely on changes in the system and data. The dyn...
BACKGROUND: Bowel-preserving local resection of early rectal cancer is less successful if the tumor infiltrates the muscularis propria as opposed to submucosal infiltration only. Magnetic resonance imaging currently lacks the spatial resolution to pr...
PURPOSE: This study developed and validated a novel deep learning radiomic biomarker to estimate response to immune checkpoint inhibitor (ICI) therapy in advanced non-small cell lung cancer (NSCLC) using real-world data (RWD) and clinical trial data.
BACKGROUND: Snakebite envenoming is a serious condition that affects 2.5 million people and causes 81,000-138,000 deaths every year, particularly in tropical and subtropical regions. The World Health Organization has set a goal to halve the deaths an...
OBJECTIVE: Preoperative prediction of visual recovery after pituitary adenoma resection surgery remains challenging. This study aimed to investigate the value of clinical and radiological features in preoperatively predicting visual outcomes after su...
INTRODUCTION: Bullous pemphigoid (BP) and prurigo nodularis (PN) are chronic pruritic skin diseases that severely impact patients' quality of life. Despite the widespread attention these two diseases have garnered within the dermatological field, the...
BACKGROUND: Cancer remains a leading cause of mortality worldwide. A non-invasive screening solution was required for early diagnosis of cancer. Multi-cancer early detection (MCED) tests have been considered to address the challenge by simultaneously...
BACKGROUND: Alzheimer's Disease (AD) poses a major challenge as a neurodegenerative disorder, and early detection is critical for effective intervention. Magnetic resonance imaging (MRI) is a critical tool in AD research due to its availability and c...
With a clinical trial failure rate of 99.6% for Alzheimer's Disease (AD), early diagnosis is critical. Machine learning (ML) models have shown promising results in early AD prediction, with survival ML models outperforming typical classifiers by prov...
Based on the Expectation Confirmation Model (ECM), this study explores the impact of perceived educational and emotional support on university students' continuance intention to engage in e-learning. Researchers conducted a survey using structured qu...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.