Mild cognitive impairment (MCI) is a significant and increasingly recognized problem in individuals with type 2 diabetes mellitus (T2DM). This study aims to develop a machine-learning model to predict MCI in patients with T2DMThe dataset was obtained...
The primary objective of this study is to employ machine learning (ML) algorithms to develop predictive models for renal function recovery in critically ill patients undergoing continuous renal replacement therapy (CRRT) due to acute kidney injury (...
Since the COVID-19 pandemic, there has been a notable resurgence of pneumonia (MPP) in children, with a concerning rise in the severity of cases. Although changes in post-pandemic respiratory infection patterns have been documented, the reasons behi...
BACKGROUND: Artificial intelligence (AI)-based cephalometric tracing has emerged as a promising tool that reduces operator variability and offers standardized, rapid, and reproducible assessments. This study aimed to evaluate the reliability and accu...
Molecular medicine (Cambridge, Mass.)
Sep 25, 2025
BACKGROUND: Juvenile idiopathic arthritis (JIA) is a rare autoimmune disease arising from a complex interplay between genetic and environmental factors. Epigenetic modifications such as DNA methylation (DNAm) have been described as potential mediator...
Magnetic resonance imaging (MRI) serves as the clinical gold standard for diagnosing lumbar disc herniation (LDH). This multicenter study was to develop and clinically validate a deep learning (DL) model utilizing axial T2-weighted lumbar MRI sequenc...
Patients with type 2 diabetes mellitus (T2DM) have a significantly higher risk of cardiovascular disease (CVD) compared to the general population. Accurately predicting this risk is crucial for developing personalized treatment plans and public healt...
To investigate the impact of retinal fluid dynamics on visual outcomes in patients with treatment-naïve neovascular age-related macular degeneration (nAMD) treated in the real world over 5 years using approved AI-based fluid monitoring. Real-world da...
This study investigates the feasibility of using tear sample analysis, based on protein corona formation on gold nanoparticles combined with electrospray ionization mass spectrometry (ESI-MS) and machine learning techniques, as a non-invasive approac...
This study aimed to validate the utility of commercially available vendor-neutral deep learning (DL) image enhancement software for improving the image quality of multiparametric MRI for gliomas in a multinational setting. A total of 294 patients fro...
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