Automated detection and recognition of oocyte toxicity by fusion of latent and observable features.
Journal:
Journal of hazardous materials
Published Date:
Apr 26, 2025
Abstract
Oocyte quality is essential for successful pregnancy, yet no discriminant criterion exists to assess the effects of environmental pollutants on oocyte abnormalities. We developed a stepwise framework integrating deep learning-extracted latent features with observable human-concept features focused on toxicity detection, subtype and strength classification. Based on 2126 murine oocyte images, this method achieves performance surpassing human capabilities with ROC-AUC of 0.9087 for toxicity detection, 0.7956-0.9034 for subtype classification with Perfluorohexanesulfonic Acid(PFHxS) achieving highest score of 0.9034 and 0.6434-0.9062 for toxicity strength classification with PFHxS achieving highest score of 0.9062. Notably, Ablation studies confirmed feature fusion improved performance by 18.7-23.4 % over single-domain models, highlighting their complementary relationship. Personalized heatmaps and feature importance revealed biomarker regions such as polar body and cortical areas aligning with clinical knowledge. AI-driven oocyte selection predicts embryo competence under pollutants, bridging computational toxicology to mitigate infertility.
