INTRODUCTION: In 2021, the International Diabetes Federation reported that 537 million people worldwide are living with diabetes. While glucagon-like peptide-1 agonists provide significant benefits in diabetes management, approximately 40% of patient...
BACKGROUND: Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening ...
International forum of allergy & rhinology
Jul 16, 2024
This follow-up dual-institutional and longitudinal study further evaluated for underlying gender biases in LORs for rhinology fellowship. Explicit and implicit linguistic gender bias was found, heavily favoring male applicants.
AIM: To develop 10-year cardiovascular disease (CVD) risk prediction models in Chinese patients with type 2 diabetes mellitus (T2DM) managed in primary care using machine learning (ML) methods.
AIMS: Assessing the risk for HF rehospitalization is important for managing and treating patients with HF. To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real-world data...
PURPOSE: This study was designed to develop and validate a machine learning-based, multimodality fusion (MMF) model using F-fluorodeoxyglucose (FDG) PET/CT radiomics and kernelled support tensor machine (KSTM), integrated with clinical factors and nu...
PURPOSE: This study was designed to investigate the prognostic significance of artificial intelligence (AI)-based quantification of myxoid stroma in patients undergoing esophageal squamous cell carcinoma (ESCC) surgery after neoadjuvant chemotherapy ...
BACKGROUND: Robust predictive models of clinical impairment and worsening in multiple sclerosis (MS) are needed to identify patients at risk and optimize treatment strategies.
BACKGROUND/AIMS: To assess the performance of deep-learning (DL) models for prediction of conversion to normal-tension glaucoma (NTG) in normotensive glaucoma suspect (GS) patients.
AIM: To predict antivascular endothelial growth factor (VEGF) treatment requirements, visual acuity and morphological outcomes in neovascular age-related macular degeneration (nAMD) using fluid quantification by artificial intelligence (AI) in a real...
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