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 …
Artificial intelligence-driven decision tree model for predicting quality of life determinants in women with endometriosis.
Endometriosis significantly impacts the quality of life (QoL) of affected women due to its complex symptomatology. This study aimed to develop a decision tree-based model to identify the key determinants …
Uncovering symptom-lesion associations through Machine learning.
to evaluate the association between symptoms and the site of endometriosis lesions using machine learning analysis DESIGN: retrospective study SETTING: Two tertiary hospitals.