This study proposes a novel approach to predict the efficacy of bevacizumab (BEV) in treating peritumoral edema in metastatic brain tumor patients by integrating advanced machine learning (ML) techniques with comprehensive imaging and clinical data. ...
With the extensive application of artificial intelligence technology in the tourism industry, robot-assisted tourism has become a vital strategy for enhancing tourist experiences and promoting sustainable tourism practices. This study aims to explore...
Heavy metal exposure is acknowledged as a risk factor for poor health. However, the effect of heavy metal exposure on the prevalence of gallstones is still unknown. Therefore, we investigated the relationship between heavy metal concentrations and th...
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....
AIMS: Studies conducted during the COVID-19 pandemic found high occurrence of suicidal thoughts and behaviours (STBs) among healthcare workers (HCWs). The current study aimed to (1) develop a machine learning-based prediction model for future STBs us...
Cancer imaging : the official publication of the International Cancer Imaging Society
May 8, 2025
OBJECTIVE: Phyllodes tumors (PTs) are rare breast tumors with high recurrence rates, current methods relying on post-resection pathology often delay detection and require further surgery. We propose a deep-learning-based Phyllodes Tumors Hierarchical...
BACKGROUND AND OBJECTIVE: Accurate detection of schizophrenia poses a grand challenge as a complex and heterogeneous mental disorder. Current diagnostic criteria rely primarily on clinical symptoms, which may not fully capture individual differences ...
BACKGROUND: Traditional statistical methods have dominated research on peripartum depression (PPD), but innovative approaches may provide deeper insights. This study aims to predict the impact factors of PPD using elastic net regression (ENR) combine...
BACKGROUND: Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG rec...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.