论文标题
人类胚泡分类在体外受精后使用深度学习
Human Blastocyst Classification after In Vitro Fertilization Using Deep Learning
论文作者
论文摘要
体外受精后（IVF）后的胚胎质量评估主要是由胚胎学家视觉进行的。但是，评估者之间的差异仍然是IVF成功率低的主要原因之一。这项研究旨在根据深度学习模型开发自动化的胚胎评估。这项研究包括来自1226个胚胎的1084张图像。受精后第3天，通过倒置显微镜捕获图像。这些图像是根据veeck标准标记的，该标准根据胚泡的大小和碎片等级将胚胎与1至5级区分开。将我们的深度学习分级结果与训练有素的胚胎学家的评分结果进行了比较，以评估模型性能。我们通过对数据集的预训练的RESNET50进行微调的最佳模型可使精度达到91.79％。呈现的模型可以在护理点设置中发展为一种自动化的胚胎评估方法。
Embryo quality assessment after in vitro fertilization (IVF) is primarily done visually by embryologists. Variability among assessors, however, remains one of the main causes of the low success rate of IVF. This study aims to develop an automated embryo assessment based on a deep learning model. This study includes a total of 1084 images from 1226 embryos. The images were captured by an inverted microscope at day 3 after fertilization. The images were labelled based on Veeck criteria that differentiate embryos to grade 1 to 5 based on the size of the blastomere and the grade of fragmentation. Our deep learning grading results were compared to the grading results from trained embryologists to evaluate the model performance. Our best model from fine-tuning a pre-trained ResNet50 on the dataset results in 91.79% accuracy. The model presented could be developed into an automated embryo assessment method in point-of-care settings.
