9.baby Family and Fertility Center, Via Dante, 15, Bologna 40125, Italy.
Department of Biology and Biotechnology 'Lazzaro Spallanzani', University of Pavia, Via Ferrata, 9 27100, Italy; Centre for Health Technology, University of Pavia, Pavia, Italy.
Reprod Biomed Online. 2021 Mar;42(3):521-528. doi: 10.1016/j.rbmo.2020.12.008. Epub 2020 Dec 24.
Can artificial intelligence and advanced image analysis extract and harness novel information derived from cytoplasmic movements of the early human embryo to predict development to blastocyst?
In a proof-of-principle study, 230 human preimplantation embryos were retrospectively assessed using an artificial neural network. After intracytoplasmic sperm injection, embryos underwent time-lapse monitoring for 44 h. For comparison, standard embryo assessment of each embryo by a single embryologist was carried out to predict development to blastocyst stage based on a single picture frame taken at 42 h of development. In the experimental approach, in embryos that developed to blastocyst or destined to arrest, cytoplasm movement velocity was recorded by time-lapse monitoring during the first 44 h of culture and analysed with a Particle Image Velocimetry algorithm to extract quantitative information. Three main artificial intelligence approaches, the k-Nearest Neighbour, the Long-Short Term Memory Neural Network and the hybrid ensemble classifier were used to classify the embryos.
Blind operator assessment classified each embryo in terms of ability to develop to blastocyst, with 75.4% accuracy, 76.5% sensitivity, 74.3% specificity, 74.3% precision and 75.4% F1 score. Integration of results from artificial intelligence models with the blind operator classification, resulted in 82.6% accuracy, 79.4% sensitivity, 85.7% specificity, 84.4% precision and 81.8% F1 score.
The present study suggests the possibility of predicting human blastocyst development at early cleavage stages by detection of cytoplasm movement velocity and artificial intelligence analysis. This indicates the importance of the dynamics of the cytoplasm as a novel and valuable source of data to assess embryo viability.
人工智能和先进的图像分析能否提取和利用源自人类早期胚胎细胞质运动的新信息，从而预测胚胎发育至囊胚阶段？
在一项原理验证研究中，使用人工神经网络对 230 个人类胚胎进行了回顾性评估。在胞浆内单精子注射后，胚胎进行了 44 小时的延时监测。为了进行比较，对每个胚胎进行了单个胚胎学家的标准胚胎评估，根据 42 小时发育时拍摄的单个图像帧预测胚胎发育至囊胚阶段。在实验方法中，在发育至囊胚或注定停滞的胚胎中，通过延时监测记录第 44 小时培养期间的细胞质运动速度，并使用粒子图像测速算法（Particle Image Velocimetry algorithm）进行分析以提取定量信息。使用了三种主要的人工智能方法，即 k-最近邻、长短期记忆神经网络和混合集成分类器来对胚胎进行分类。
盲法操作员评估根据胚胎发育至囊胚的能力对每个胚胎进行分类，准确率为 75.4%，灵敏度为 76.5%，特异性为 74.3%，精密度为 74.3%，F1 得分为 75.4%。将人工智能模型的结果与盲法操作员分类相结合，准确率为 82.6%，灵敏度为 79.4%，特异性为 85.7%，精密度为 84.4%，F1 得分为 81.8%。
本研究表明，通过检测细胞质运动速度和人工智能分析，有可能在早期卵裂阶段预测人类囊胚的发育。这表明细胞质的动力学作为评估胚胎活力的新的有价值的数据来源的重要性。
