Onthuam Krittapat, Charnpinyo Norrawee, Suthicharoenpanich Kornrapee, Engphaiboon Supphaset, Siricharoen Punnarai, Chaichaowarat Ronnapee, Suebthawinkul Chanakarn
International School of Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand.
Department of Marine Technology, Norwegian University of Science and Technology, 7491 Trondheim, Norway.
J Imaging. 2025 Jan 7;11(1):13. doi: 10.3390/jimaging11010013.
The current process of embryo selection in in vitro fertilization is based on morphological criteria; embryos are manually evaluated by embryologists under subjective assessment. In this study, a deep learning-based pipeline was developed to classify the viability of embryos using combined inputs, including microscopic images of embryos and additional features, such as patient age and developed pseudo-features, including a continuous interpretation of Istanbul grading scores by predicting the embryo stage, inner cell mass, and trophectoderm. For viability prediction, convolution-based transferred learning models were employed, multiple pretrained models were compared, and image preprocessing techniques and hyperparameter optimization via Optuna were utilized. In addition, a custom weight was trained using a self-supervised learning framework known as the Simple Framework for Contrastive Learning of Visual Representations (SimCLR) in cooperation with generated images using generative adversarial networks (GANs). The best model was developed from the EfficientNet-B0 model using preprocessed images combined with pseudo-features generated using separate EfficientNet-B0 models, and optimized by Optuna to tune the hyperparameters of the models. The designed model's F1 score, accuracy, sensitivity, and area under curve (AUC) were 65.02%, 69.04%, 56.76%, and 66.98%, respectively. This study also showed an advantage in accuracy and a similar AUC when compared with the recent ensemble method.
目前体外受精中的胚胎选择过程基于形态学标准；胚胎由胚胎学家在主观评估下进行人工评估。在本研究中，开发了一种基于深度学习的流程，使用包括胚胎显微图像和其他特征（如患者年龄）以及通过预测胚胎阶段、内细胞团和滋养外胚层对伊斯坦布尔分级分数进行连续解释而生成的伪特征等组合输入来对胚胎的活力进行分类。对于活力预测，采用了基于卷积的迁移学习模型，比较了多个预训练模型，并利用图像预处理技术和通过Optuna进行超参数优化。此外，使用称为视觉表征对比学习简单框架（SimCLR）的自监督学习框架与使用生成对抗网络（GAN）生成的图像合作训练了一个自定义权重。最佳模型是从EfficientNet - B0模型开发而来，使用预处理图像与使用单独的EfficientNet - B0模型生成的伪特征相结合，并通过Optuna进行优化以调整模型的超参数。设计模型的F1分数、准确率、灵敏度和曲线下面积（AUC）分别为65.02%、69.04%、56.76%和66.98%。与最近的集成方法相比，本研究在准确率方面也显示出优势，且AUC相似。
