Background: Endometriosis is a chronic gynecological condition marked by ectopic endometrial-like tissue, leading to inflammation, pain, and infertility. Diagnosis is often delayed by up to 10 years. Identifying non-invasive biomarkers …
Evaluation of Pyroptosis-Associated Genes in Endometrial Cancer Utilizing a 101-Combination Machine Learning Framework and Multi-Omics Data Frontiers
Identification and validation of a novel machine learning model for predicting severe pelvic endometriosis: A retrospective study Nature
Endometriosis (EM) significantly impacts the quality of life, and its diagnosis currently relies on surgery, which carries risks and may miss early lesions. Noninvasive biomarkers are urgently needed for early …
Artificial intelligence (AI) may have the potential to improve existing diagnostic challenges in endometriosis imaging. To better direct future research, this descriptive review summarizes the general landscape of AI applications …
Endometriosis (EM) is the most common gynecological disease in women of childbearing age. This study aims to identify key genes and screen drugs that may contribute to EM treatment.
Endometriosis (EM) is a chronic gynecological disorder characterized by the growth of endometrial-like tissue outside the uterus, leading to pain and infertility. Recent studies suggest that antioxidants may play a …
Endometriosis and Recurrent Implantation Failure (RIF) are both pivotal clinical issues within the realm of reproductive medicine, sharing significant overlap in their pathophysiological mechanisms. However, research exploring the commonalities between …
Association of composite dietary antioxidant index and endometriosis risk in reproductive - age women: a cross-sectional study using big data-machine learning approach Frontiers
Early diagnosis and treatment of endometriosis (EM) remain challenging because of the lack of knowledge about EM development. While oxidative stress (OS) has been associated with EM, the link is …