Approaches to reliably predict the developmental potential of embryos and select suitable embryos for blastocyst culture are needed. The development of time-lapse monitoring (TLM) and artificial intelligence (AI) may help solve this problem. Here, we report deep learning models that can accurately predict blastocyst formation and usable blastocysts using TLM videos of the embryo’s first three days. The DenseNet201 network, focal loss, long short-term memory (LSTM) network and gradient boosting classifier were mainly employed, and video preparation algorithms, spatial stream and temporal stream models were developed into ensemble prediction models called STEM and STEM + . STEM exhibited 78.2% accuracy and 0.82 AUC in predicting blastocyst formation, and STEM + achieved 71.9% accuracy and 0.79 AUC in predicting usable blastocysts. We believe the models are beneficial for blastocyst formation prediction and embryo selection in clinical practice, and our modeling methods will provide valuable information for analyzing medical videos with continuous appearance variation.

超声成像作为常见的医学成像技术,在诊断和治疗疾病方面发挥着重要作用.人工判读超声图像依赖于医生主观经验知识,其结果存在观察者间和观察者内的差异,并且过程耗时耗力,难以满足快速、批量的临床诊断需求.随着人工智能的快速发展,基于深度学习的图像分割模型被广泛地应用于医学图像分割领域,并取得了不俗成果.基于U-Net深度神经网络,对其结构进行优化改进,构建适用于超声图像正中神经分割的卷积神经网络模型.在网络中添加了规范层和丢弃层,下采样的最大池化层替换成卷积层;同时,采用指数线性单元激活函数,使用循环学习率,以增加网络模型的鲁棒性.测试结果表明,改进的U-Net模型在超声正中神经图像的自动分割方面表现良好,横切面、纵切面的Dice系数分别达到了78％与89％.该方法有望用于临床超声神经图像的辅助分析.

目的:运用改进U-Net深度网络学习定量评价腕管综合征正中神经的超声图像,确定基于改进U-Net深度网络学习的卷积神经网络模型,探讨其在定量评估腕管综合征正中神经卡压中的应用价值.方法:搜集213例经肌电图确诊的腕管综合征正中神经卡压患者及104例健康志愿者,213例正中神经卡压患者中60例为双侧卡压.对317例受检者行超声检查,在腕管处保存超声图像,共得到正中神经图像377组.由擅长肌骨超声的医师对377组图像进行勾勒.应用基于改进U-net深度网络学习的卷积神经网络模型,分割腕管综合征卡压的正中神经超声图像,定量分析提取横切以及纵切的正中神经超声图像的影像组学量化特征.结果:改进的U-Net深度网络可以很好地识别切割正中神经;改进的U-Net深度网络可以定量表示CTS中卡压的正中神经回声减低,区域明暗参数A、明暗参数I、对比明暗参数RI以及纹理参数Homo、纹理不均匀参数Cont差异均有统计学意义(P=0.000).结论:改进的U-Net模型在超声正中神经图像自动分割方面表现良好,可以定量分析腕管综合征正中神经卡压时灰度以及神经纹理均匀性.