Advancements in Artificial Intelligence in the study of Endometrium

Objective Findings on the application of artificial intelligence (AI), particularly convolutional neural networks (CNNs), in enhancing diagnostic and prognostic capabilities in gynecological health were synthesized. Design Recent technological advancements, particularly AI and machine learning, in the study and management of endometrial conditions were reviewed. Subjects Various studies exploring the role of AI in diagnosing and managing endometrial conditions such as endometriosis, endometrial receptivity, and endometrial cancer were examined. Intervention The development and implementation of CNNs, radiomics models, and integration of omics data (proteomics, metabolomics, transcriptomics), ultrasonographic imaging, in endometrial studies were analyzed. Main Outcomes Diagnostic accuracy, prognostic assessments, early detection, personalized treatment, and clinical management of endometrial conditions were evaluated. Results It was found that AI technologies, surpassing manual methods in accuracy, enhance the classification of endometrial patterns and analysis of uterine peristalsis. The quantitative assessment of endometrial vascularization and blood supply is improved by AI, leading to better predictions for pregnancy outcomes. Traditional challenges, such as time-consuming manual measurements and significant inter-observer variability, are mitigated by AI-assisted ultrasound, which provides automated detection and measurement of follicles, reducing examination time and improving reproducibility. Diagnostic accuracy in follicular monitoring and endometrial receptivity (ER) assessment is enhanced by AI models, though challenges remain, including the need for robust AI models and validation across diverse populations. The integration of AI with transcriptomic testing and biomarkers in assisted reproductive technology (ART) shows promise in improving embryo transfer timing and personalized treatment strategies. In endometrial cancer and hyperplasia, AI models significantly enhance diagnostic accuracy, sensitivity, and specificity, improving preoperative risk classification and prognostication. Non-invasive diagnostic methods like proteomic profiling and AI models demonstrate high sensitivity and specificity for endometriosis, potentially reducing the need for invasive procedures. Conclusions It has been demonstrated that AI models, particularly those leveraging deep learning, show promise in enhancing diagnostic efficiency, predicting molecular subtypes, and improving clinical outcomes in gynecological cancers and reproductive health. However, challenges such as model generalization, data standardization, and interpretability need to be addressed. Future research should focus on validating these models and integrating them into clinical workflows to optimize patient care.