RESUMEN
PURPOSE: To determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data. METHODS: This retrospective study utilized a dataset of 1908 blastocyst embryos. The dataset includes ploidy status, morphokinetic features, morphology grades, and 11 clinical variables. Six machine learning (ML) models including Random Forest (RF), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Support Vector Machine (SVM), AdaBoost (ADA), and Light Gradient-Boosting Machine (LGBM) were trained to predict ploidy status probabilities across three distinct datasets: high-grade embryos (HGE, n = 1107), low-grade embryos (LGE, n = 364), and all-grade embryos (AGE, n = 1471). The model's performance was interpreted using XAI, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) techniques. RESULTS: The mean maternal age was 38.5 ± 3.85 years. The Random Forest (RF) model exhibited superior performance compared to the other five ML models, achieving an accuracy of 0.749 and an AUC of 0.808 for AGE. In the external test set, the RF model achieved an accuracy of 0.714 and an AUC of 0.750 (95% CI, 0.702-0.796). SHAP's feature impact analysis highlighted that maternal age, paternal age, time to blastocyst (tB), and day 5 morphology grade significantly impacted the predictive model. In addition, LIME offered specific case-ploidy prediction probabilities, revealing the model's assigned values for each variable within a finite range. CONCLUSION: The model highlights the potential of using XAI algorithms to enhance ploidy prediction, optimize embryo selection as patient-centric consultation, and provides reliability and transparent insights into the decision-making process.
RESUMEN
Osteopontin (OPN) isoforms, including OPNb and OPNc, promote malignancy and may contribute to the pathogenesis of endometriosis, a benign disorder with multiple characteristics resembling malignant tumors. In our experiments, OPNb and OPNc were significantly overexpressed in both endometriosis and adenomyosis compared to the normal endometrium. Upregulation of CD44v and the epithelial-mesenchymal transition (EMT) process was also present in endometriotic lesions. Overexpression of OPNb and OPNc splicing variants in endometriotic cells evoked morphological changes, actin remodeling, cell proliferation, cell migration, and EMT through binding OPN ligand receptors CD44 and αvß3, subsequently activating the PI3K and NF-ĸB pathways. We elucidated the causal role of OPN splice variants in regulating endometriotic cell growth, which may promote the development of OPN-targeted therapies for patients suffering from endometriotic disorders.