To systematically evaluate the methodological quality and diagnostic performance of artificial intelligence (AI) applications, specifically machine learning (ML) and deep learning (DL), in the diagnosis of endometriosis through imaging and …
Endometriosis (EMs) affects approximately 10% of reproductive-age women worldwide, yet its pathogenesis remains incompletely understood. Abnormal cell differentiation and somatic mutations in the ectopic endometrial microenvironment play critical roles in …
Endometriosis (EM) is a chronic, estrogen-dependent disease that lacks reliable noninvasive diagnostic biomarkers. This study was aimed at evaluating the diagnostic value of PAX8 using integrated transcriptomic and machine learning …
Endometriosis (EMS) is a common gynecological disease that seriously affects women's health and quality of life. However, the detailed dynamic cellular and molecular mechanisms underlying EMS pathogenesis remain largely unknown. …
Precise staging of endometriosis remains a clinical challenge, as current diagnosis depends almost entirely on laparoscopic visualization-an invasive procedure marked by considerable inter-observer disagreement and diagnostic delays. Existing non-invasive approaches, …
To screen immune-related biomarkers in diagnosing patients with both endometriosis (EM) and systemic lupus erythematosus (SLE).
To explore the material basis and network mechanism of the Guizhi Fuling pills in the treatment of endometriosis and endometrial polyps based on network pharmacology and machine learning. The effective …
Endometriosis and polycystic ovary syndrome (PCOS) are common, multifactorial gynecological disorders shaped by endocrine imbalance, immune dysfunction, metabolic disruption, genetic susceptibility, and environmental exposures. Despite their major contribution to infertility …
Endometriosis affects a large number of women of reproductive age, and its pathogenesis is still unclear. It causes severe chronic pelvic pain, which is often misdiagnosed as irritable bowel syndrome, …