OBJECTIVE: This study validated the artificial intelligence (AI)-based algorithm LuxIA for screening more-than-mild diabetic retinopathy (mtmDR) from a single 45° colour fundus image of patients with diabetes mellitus (DM, type 1 or type 2) in Spain....
PURPOSE: This study evaluates the impact of artificial intelligence (AI) assistance on the diagnostic performance of radiologists with varying levels of experience in interpreting mammograms in a Malaysian tertiary referral center, particularly in wo...
This study aimed to demonstrate whether plasma galectin-3 could predict the development of postoperative delirium (POD) in patients with acute aortic dissection (AAD). Prospective, observational study. Cardiac surgery intensive care unit. Consecutive...
Rapidly increasing healthcare spending globally is significantly driven by high-need, high-cost (HNHC) patients, who account for the top 5% of annual healthcare costs but over half of total expenditures. The programs targeting existing HNHC patients ...
ObjectiveThe prediction of early response in locally advanced nasopharyngeal carcinoma (LA-NPC) after concurrent chemoradiotherapy (CCRT) is important for determining the need for timely consolidation therapy. We developed a radiomic analysis of mult...
OBJECTIVES: To identify the factors associated with post-stroke depression (PSD) and develop a machine learning predictive model using a large dataset, considering sociodemographic, lifestyle, and clinical factors.
Small molecules have been playing a crucial role in drug discovery; however, some exhibit nonspecific inhibitory effects during hit screening due to the formation of colloidal aggregators. Such false positives often lead to significant research costs...
BACKGROUND: The early prediction of lymph node positivity (LN+) after neoadjuvant chemotherapy (NAC) is crucial for optimizing individualized treatment strategies. This study aimed to integrate radiomic features and clinical biomarkers through machin...