Diagnosis of endometriosis faces significant challenges including diagnostic delay and reliance on invasive procedures. Deep endometriosis (DE) poses additional difficulties in non-invasive diagnosis due to its subtle and complex imaging …
Background: Women's health has historically served as an incubator for major medical innovations yet often faces relative neglect in sustained funding and implementation. The rise of artificial intelligence (AI) and …
Integrating inflammatory biomarkers and demographic variables with machine learning to predict endometriosis risk Nature
This study explores the relationship between inflammatory biomarkers and the risk of endometriosis, aiming to develop a predictive model using National Health and Nutrition Examination Survey (1999-2006) data. The dataset …
The causal bridge from environmental exposure to endometriosis (Ems) biology remains incompletely defined. Di(2-ethylhexyl) phthalate (DEHP) is repeatedly implicated in elevated Ems risk, yet actionable molecular anchors linking exposure to …
Background: Adenomyosis remains difficult to diagnose non-invasively due to clinical overlap with endometriosis and the limited specificity of imaging techniques. This pilot study evaluated whether serum- and urine-derived microRNA (miRNA) …
This study evaluated the diagnostic potential of Fourier-transform infrared (FTIR) spectroscopy combined with machine learning for the detection of ovarian, bowel, and peritoneal endometriosis. The Boruta algorithm was applied to …
Can a serum miRNA signature serve as a potential diagnostic biomarker for endometriosis (END)?
Endometriosis (EM), a prevalent gynecological disorder in reproductive-age women, lacks reliable noninvasive diagnostic tools. EM may be detected by neutrophil extracellular traps (NETs), which are essential to inflammation and immunological …
Development of a senescence-related lncRNA signature in endometrial cancer based on multiple machine learning models Frontiers