Raes Annelies, Babin Danilo, Pascottini Osvaldo Bogado, Opsomer Geert, Van Soom Ann, Smits Katrien
Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium.
Department of Telecommunications and Information Processing - Image Processing and Interpretation, Ghent University-imec, Ghent, Belgium.
Sci Rep. 2025 Jul 1;15(1):21829. doi: 10.1038/s41598-025-09019-6.
Evaluating cumulus-oocyte complex (COC) morphology is commonly used to assess oocyte quality. However, clear guidelines on interpreting COC morphology data are lacking as this evaluation method is subjective. In the present study, individual in vitro embryo production was used, allowing follow-up of blastocyst formation for each COC. Images of immature COCs were presented to embryologists and two artificial intelligence (AI) models: deep neural network (DNN) and random forest classifier (RF). The aims were to (1) determine the most relevant morphological characteristics in distinguishing qualitative COCs, (2) review human-made predictions, and (3) build predictive AI models. Our experiments identified cumulus size as pivotal characteristic of COC quality, while embryologists assigned ooplasm morphology as most important. Inspection of COCs by the human eye showed significant limitations, as evidenced by their low predictive ability (balanced accuracy: 42.9%) and fair reliability. Our AI models outperformed the embryologists, yielding a balanced accuracy of 79.3% and 71.2% for DNN and RF, respectively. The first AI models that successfully predict developmental competence of immature bovine oocytes were created, outperforming embryologists and offering an objective perspective for COC morphology assessment. AI has emerged as a novel tool for oocyte appreciation, assisting decision-making in the embryology lab.
评估卵丘-卵母细胞复合体（COC）形态通常用于评估卵母细胞质量。然而，由于这种评估方法具有主观性，因此缺乏关于解释COC形态数据的明确指南。在本研究中，采用了个体体外胚胎生产方法，从而能够对每个COC的囊胚形成进行跟踪。将未成熟COC的图像呈现给胚胎学家以及两种人工智能（AI）模型：深度神经网络（DNN）和随机森林分类器（RF）。目的是：（1）确定区分优质COC的最相关形态特征；（2）审查人工预测结果；（3）建立预测性AI模型。我们的实验确定卵丘大小是COC质量的关键特征，而胚胎学家则认为卵质形态最为重要。肉眼检查COC显示出明显的局限性，其预测能力较低（平衡准确率：42.9%）且可靠性一般便是证明。我们的AI模型表现优于胚胎学家，DNN和RF的平衡准确率分别为79.3%和71.2%。创建了首个成功预测未成熟牛卵母细胞发育能力的AI模型，其表现优于胚胎学家，并为COC形态评估提供了客观视角。AI已成为评估卵母细胞的一种新工具，有助于胚胎学实验室的决策制定。
