Machine learning and microfluidic integration for oocyte quality prediction

Despite advancements in in vitro fertilization (IVF) over the past 30 years, its outcome effectiveness remains low (20-40%). This study introduces a microfluidic-based machine learning framework to improve predictive accuracy in oocyte quality assessment. Immature oocytes were recorded as they passed through a custom-designed microfluidic channel under controlled flow. Using image processing, two biomechanical features-Cortical Tension (CT) and Deformation Index (DI)-were extracted. Additionally, oocyte diameter and the critical flow rate (Q)-defined as the minimum flow rate necessary for an oocyte to pass through the channel-were included as predictive variables. A dataset of 54 oocytes was labeled based on maturation, fertilization, and cleavage outcomes. Supervised learning models (Random Forest, Decision Tree, K-Nearest Neighbors, eXtreme Gradient Boosting, Logistic Regression, Naive Bayes, Support Vector Machines, and Light Gradient Boosting Machine were evaluated. Random Forest achieved the best classification accuracy: 76.10% (K-Fold) and 75.93% (Leave-One-Out). For unsupervised learning, K-Means, DBSCAN, Agglomerative Clustering, and Gaussian Mixture Models were applied. Among them, Agglomerative Clustering yielded the best performance (Silhouette = 0.49, Davies-Bouldin = 0.73), showing meaningful grouping patterns among oocytes. These results demonstrate that integrating biomechanical profiling with machine learning can significantly improve the objectivity and accuracy of oocyte quality prediction. This approach holds promise for enhancing embryo selection strategies in Assisted Reproductive Technology (ART) and optimizing In Vitro Fertilization (IVF) outcomes.