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Integration of Raman tweezers and machine learning for label-free single-cell characterization of endometriosis cells.

Endometriosis occurs when endometrial tissue grows outside the uterus, affecting millions of women worldwide. Despite extensive research, its cellular mechanisms remain unclear, complicating both diagnosis and treatment. This study presents …

Published: Feb. 19, 2026, midnight
Correction: Diagnostic accuracy of machine learning for endometriosis: a systematic review and meta-analysis.

[This corrects the article DOI: 10.3389/fendo.2025.1735567.].

Published: Feb. 18, 2026, midnight
Study on biomarkers associated with epigenetic factors in endometriosis combining transcriptome with experimental validation.

Endometriosis (EM) is a disease related to reproductive dysfunction. The mechanism of epigenetic factors (EF) in EM still needs to be studied. Emerging evidence suggests that EF plays a role …

Published: Feb. 3, 2026, midnight
Serum metabolic fingerprinting for diagnosis and therapeutic applications of ovarian endometriosis.

Ovarian endometriosis (OvE) is a gynecological disorder with endometrial tissue in the ovaries, for which effective non-invasive diagnosis and curative treatments are currently lacking. Serum samples were collected from both …

Published: Feb. 2, 2026, midnight
Diagnostic accuracy of machine learning for endometriosis: a systematic review and meta-analysis.

Researchers have explored machine learning (ML) in diagnosing endometriosis. However, systematic evidence on its diagnostic accuracy for endometriosis remains scarce.

Published: Jan. 27, 2026, midnight
An ultrasound-based machine learning model for predicting pelvic adhesions: A SHAP-enhanced XGBoost approach.

This study is the first to develop and evaluate a machine learning (ML) model for predicting pelvic adhesions based on ultrasound features, utilizing the SHapley Additive Explanations (SHAP) framework for …

Published: Jan. 19, 2026, midnight
Urinary microRNAs for the non-invasive diagnosis of endometriosis identified by next-generation sequencing and machine learning.

Endometriosis is a chronic gynecological disease associated with pain, infertility, and delayed diagnosis. Non-invasive biomarkers are urgently needed to facilitate earlier detection and reduce the reliance on diagnostic laparoscopy. MicroRNAs …

Published: Jan. 7, 2026, midnight
FTIR Spectroscopy Combined with Machine Learning Reveals Molecular Signatures Distinguishing three Phenotypes of Endometriosis.

Endometriosis is a chronic inflammatory disorder in which endometrial tissue grows outside the uterus, leading to pelvic pain and infertility. It remains a major challenge in women's health due to …

Published: Jan. 5, 2026, midnight
AI-Enhanced MRI Radiomics for Discriminating Active and Fibrotic Lesions to Support Therapeutic Decision-Making in Deep Endometriosis.

This study investigates whether quantitative analysis of preoperative Magnetic Resonance Imaging (MRI) scans can differentiate deep infiltrating endometriosis (DIE) lesion types (active or fibrotic) and associate them with reported pain …

Published: Jan. 3, 2026, midnight
ETS Homologous Factor (EHF) and Gamma Linolenic Acid (GLA): novel strategies for early diagnosis and treatment of endometriosis.

Published: Jan. 2, 2026, midnight
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