A machine learning approach towards endometriosis screening using infrared spectra of urine.
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 …
Machine learning prediction of clinical pregnancy in endometriosis patients following fresh IVF/ICSI-ET.
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, …
Initial results in the automatic visual recognition of endometriosis lesions by artificial intelligence during laparoscopy: a proof-of-concept study.
To develop a machine learning method for the automatic recognition of endometriosis lesions during laparoscopic surgery and evaluate its feasibility and performance.
Effects of Mono- (2-ethylhexyl) phthalate and Phthalic Acid Monobenzyl Ester on endometriosis using network toxicology, machine learning and molecular docking techniques.
Phthalate metabolites Mono- (2-ethylhexyl) phthalate(MEHP) and Phthalic Acid Monobenzyl Ester (MBZP) are widely present in the environment, can interfere with the endocrine system and accumulate in human tissues, and are …