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TongueNet-GYN: a multimodal deep learning framework for non-invasive gynecological disease screening in digital public health.

Gynecological diseases, such as polycystic ovary syndrome (PCOS) and endometriosis, are prevalent global health concerns. Conventional diagnostics often rely on invasive procedures or costly imaging, limiting accessibility in resource-constrained settings. …

Published: June 23, 2026, midnight
Emerging Pathways to Non-Invasive Diagnosis in Endometriosis: Integrating Machine Learning, Deep Learning and Multi-Omics Biomarkers.

Endometriosis is a chronic, debilitating condition affecting approximately 10-15% of reproductive-aged women and it is often associated with significant diagnostic delays due to its heterogeneity and unreliable non-invasive tests. Artificial …

Published: June 12, 2026, midnight
Deciphering immune-inflammatory dysregulation in the endometriotic microenvironment: insights from single-cell omics and artificial intelligence.

Endometriosis is a prevalent chronic inflammatory gynecological disorder affecting approximately 10% of reproductive-age women worldwide, characterized by endometrial-like tissue outside the uterine cavity. Ectopic lesion growth tracks closely with immune-inflammatory …

Published: June 11, 2026, midnight
Artificial intelligence potential in ovarian endometriosis imaging: a comparative meta-analysis of transvaginal ultrasound-based AI models and human readers.

Transvaginal ultrasound (TVUS) is widely used for diagnosing ovarian endometriosis but remains limited by significant operator dependency. This systematic review and meta-analysis evaluated the diagnostic accuracy of ultrasound-based artificial intelligence …

Published: May 26, 2026, midnight
Bridging the Gap Between Artificial Intelligence and Clinical Readiness in Endometriosis Diagnosis: A Systematic Review.

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 …

Published: April 30, 2026, midnight
Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation.

Gynecological diseases represent a persistent global health burden. According to a WHO report, the global incidence of gynecological diseases exceeds 65%. Furthermore, over 90% of women suffer from gynecological issues …

Published: April 17, 2026, midnight
Non-invasive endometriosis staging prediction using integrated radiomics and spatiotemporal transformer model based on dynamic contrast-enhanced MRI.

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, …

Published: April 9, 2026, midnight
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
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
Vascular graph network for ovarian lesion classification using optical-resolution photoacoustic microscopy.

Diagnosing ovarian lesions is challenging because of their heterogeneous clinical presentations. Some benign ovarian conditions, such as endometriosis, can have features that mimic cancer. We use optical-resolution photoacoustic microscopy (OR-PAM) …

Published: Dec. 30, 2025, midnight
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