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A Machine Learning-Based Prediction Model for Deep Infiltrating Endometriosis.

Deep infiltrating endometriosis (DIE) is a severe endometriosis phenotype. This study aimed to develop and internally validate a machine learning-based predictive model for DIE using retrospective clinical data from a …

Published: Oct. 1, 2026, midnight
Machine learning-assisted design of a novel crocin-loaded poloxamer 407/hyaluronic acid in situ forming hydrogel for endometriosis treatment: in vitro characterization and in vivo efficacy evaluation.

Our study focused on fabricating and characterizing a crocin in situ forming hydrogel for endometriosis treatment. An artificial neural network (ANN)-based machine learning (ML) approach was employed to determine the …

Published: Sept. 28, 2026, midnight
Artificial intelligence for women's reproductive health: A scoping review of global diagnostic trends, methodological gaps, and a translational research agenda for low-resource settings.

Women's reproductive and endocrine disorders including Polycystic Ovary Syndrome (PCOS), endometriosis, thyroid disorders, infertility, and pregnancy-related complications remain a major global health burden. These conditions are especially difficult to manage …

Published: Sept. 25, 2026, midnight
Integrative analysis and experiment validation of SLC12A8 as a biomarker for the malignant transition from endometriosis to endometriosis associated ovarian cancer.

Endometriosis (EM) is a chronic inflammatory, estrogen‑dependent benign gynecological disorder. A subset of patients with EM may subsequently develop endometriosis‑associated ovarian cancer (EAOC), implying a biological continuum between these two …

Published: Sept. 15, 2026, midnight
Predicting Endometriosis Status and Menstrual Cycle Phase Using DNA Methylation.

Endometriosis is a chronic inflammatory disease associated with pelvic pain, infertility, and delayed diagnosis. Growing evidence suggests that altered DNA methylation contributes to disease development and could serve as a …

Published: Sept. 7, 2026, midnight
The Phendo Project: How FemTech Is Advancing Endometriosis Research and Women's Health.

FemTech continues to evolve and advance despite persistent research and funding gaps. In this News and Perspectives article, JMIR Correspondent Jenny Castillo Cato reports on an innovative, cross-disciplinary research project …

Published: Aug. 26, 2026, midnight
Machine learning-integrated molecular subtyping reveals two biologically distinct endometriosis subtypes in the EndometDB database.

Endometriosis affects approximately 10% of reproductive-age women, with a diagnostic delay of 7-10 years. Despite clinical heterogeneity, current rASRM staging poorly predicts treatment outcomes. Molecular subtyping may reveal biologically meaningful …

Published: Aug. 26, 2026, midnight
Phenotypic Analysis of Human and Murine Endometrial Organoids using a Machine Learning Approach.

Endometrial organoids are a powerful, 3D in vitro system for studying reproductive function and disease since they are more physiologically representative than 2D cell models. However, optimal methods for the …

Published: Aug. 19, 2026, midnight
The Role of Artificial Intelligence in the Radiological Diagnosis of Urogynecological and Obstetric Disorders: A Narrative Review.

Artificial intelligence (AI) has emerged as a transformative tool in radiological diagnosis, particularly in urogynaecology and obstetric disorders where accurate and timely imaging is essential. This narrative review evaluates current …

Published: Aug. 18, 2026, midnight
Bridging the Diagnostic Gap: Reviewing Current Endometriosis Screening Tools and Models.

Background: Endometriosis affects 10% of reproductive-aged women globally and is one of the major causes of infertility. It is a debilitating, chronic condition with an average diagnostic delay of up …

Published: Aug. 11, 2026, midnight
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