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IITM Pravartak Announces Batch 03 of Applied Artificial Intelligence and Deep Learning Programme to Build Enterprise-Ready AI Talent - NewsX

IITM Pravartak Announces Batch 03 of Applied Artificial Intelligence and Deep Learning Programme to Build Enterprise-Ready AI Talent NewsX

Published: June 17, 2026, 1:18 p.m.
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
Deep learning-based automated detection of endometrioid endometrial carcinoma in histopathology - Frontiers

Deep learning-based automated detection of endometrioid endometrial carcinoma in histopathology Frontiers

Published: Dec. 8, 2025, 10:41 a.m.
Detection of peritoneal, ovarian, and bowel endometriosis using FTIR spectroscopy and machine learning.

This study evaluated the diagnostic potential of Fourier-transform infrared (FTIR) spectroscopy combined with machine learning for the detection of ovarian, bowel, and peritoneal endometriosis. The Boruta algorithm was applied to …

Published: Nov. 23, 2025, midnight
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