This study aims to develop a machine learning-based predictive model for patients with endometriosis, with the goal of precisely identifying key factors and reliable predictive markers that influence live birth …
Accurate diagnosis of pathology from ultrasound images is reliant upon images of a suitable diagnostic quality being acquired. This study aimed to create a novel machine learning model to automatically …
Endometriosis (EMs) is a gynecological disorder characterized by chronic inflammation and an aberrant immune microenvironment. In this study, we integrated the GSE6364 dataset from the GEO database to identify differentially …
Identification and Validation of Lactate-related gene signatures in endometriosis for clinical valuation and immune characterization by WGCNA and machine learning Frontiers
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To explore B cell infiltration-related genes in endometriosis (EM) and investigate their potential as diagnostic biomarkers.
Endometriosis diagnosis is challenging due to non-specific symptoms that overlap with other gynaecological conditions. This study proposes a non-invasive Machine Learning (ML) ‒ based urine test using Attenuated Total Reflection …
Fresh embryo transfer reduces waiting time and minimizes embryo cryodamage for endometriosis (EM) patients. The current prediction models for fresh embryo transfer outcomes in EM primarily rely on logistic regression, …
To develop a machine learning method for the automatic recognition of endometriosis lesions during laparoscopic surgery and evaluate its feasibility and performance.
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