AIMC Topic: Endometriosis

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Endometriosis Online Communities: How Machine Learning Can Help Physicians Understand What Patients Are Discussing Online.

Journal of minimally invasive gynecology
STUDY OBJECTIVE: Use machine learning to characterize the content of endometriosis online community posts and comments.

Identification of programmed cell death-related genes and diagnostic biomarkers in endometriosis using a machine learning and Mendelian randomization approach.

Frontiers in endocrinology
BACKGROUND: Endometriosis (EM) is a prevalent gynecological disorder frequently associated with irregular menstruation and infertility. Programmed cell death (PCD) is pivotal in the pathophysiological mechanisms underlying EM. Despite this, the preci...

Discriminative diagnosis of ovarian endometriosis cysts and benign mucinous cystadenomas based on the ConvNeXt algorithm.

European journal of obstetrics, gynecology, and reproductive biology
PURPOSE: The objective of this study was to develop a deep learning model, using the ConvNeXt algorithm, that can effectively differentiate between ovarian endometriosis cysts (OEC) and benign mucinous cystadenomas (MC) by analyzing ultrasound images...

FEMaLe: The use of machine learning for early diagnosis of endometriosis based on patient self-reported data-Study protocol of a multicenter trial.

PloS one
INTRODUCTION: Endometriosis is a chronic disease that affects up to 190 million women and those assigned female at birth and remains unresolved mainly in terms of etiology and optimal therapy. It is defined by the presence of endometrium-like tissue ...

Identification of diagnostic markers related to inflammatory response and cellular senescence in endometriosis using machine learning and in vitro experiment.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]
OBJECTIVE: To understand the association between chronic inflammation, cellular senescence, and immunological infiltration in endometriosis.

Mining phase separation-related diagnostic biomarkers for endometriosis through WGCNA and multiple machine learning techniques: a retrospective and nomogram study.

Journal of assisted reproduction and genetics
OBJECTIVE: The objective of this study was to investigate the role of phase separation-related genes in the development of endometriosis (EMs) and to identify potential characteristic genes associated with the condition.

Robotic surgery for bowel endometriosis: a multidisciplinary management of a complex entity.

Techniques in coloproctology
BACKGROUND: Bowel endometriosis impacts quality of life. Treatment requires complex surgical procedures with associated morbidity. Precision approach with robotic surgery leads to organ preservation. Bowel endometriosis requires a multidisciplinary m...

Noninvasive diagnostic imaging for endometriosis part 1: a systematic review of recent developments in ultrasound, combination imaging, and artificial intelligence.

Fertility and sterility
Endometriosis affects 1 in 9 women and those assigned female at birth. However, it takes 6.4 years to diagnose using the conventional standard of laparoscopy. Noninvasive imaging enables a timelier diagnosis, reducing diagnostic delay as well as the ...

Robot-assisted laparoscopy does not have demonstrable advantages over conventional laparoscopy in endometriosis surgery: a systematic review and meta-analysis.

Surgical endoscopy
BACKGROUND: Endometriosis is a chronic condition affecting 6-10% of women of reproductive age, with endometriosis-related pain and infertility being the leading symptoms. Currently, the gold standard treatment approach to surgery is conventional lapa...

First experience with the Hugo™ robot-assisted surgery system for endometriosis: A descriptive study.

Acta obstetricia et gynecologica Scandinavica
INTRODUCTION: The Medtronic Hugo™ Robot-assisted Surgery (RAS) system was recently approved for clinical use. We explored the safety and feasibility of this system for endometriosis surgery. The primary outcome was safe case completion without major ...