Research question

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?

Design

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.

Results

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.

Conclusions

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.

中文翻译：

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早期人类胚胎的细胞质运动：成像和人工智能预测囊胚发育

研究问题

人工智能和先进的图像分析能否提取和利用源自早期人类胚胎细胞质运动的新信息来预测胚泡的发育？

设计

在一项原理验证研究中，使用人工神经网络对 230 个人类植入前胚胎进行了回顾性评估。胞浆内单精子注射后，胚胎接受延时监测 44 小时。为了进行比较，由单个胚胎学家对每个胚胎进行标准胚胎评估，以根据在发育 42 小时时拍摄的单个相框预测发育到囊胚阶段。在实验方法中，在发育到囊胚或注定要停滞的胚胎中，在培养的前 44 小时内通过延时监测记录细胞质运动速度，并使用粒子图像测速算法进行分析以提取定量信息。三种主要的人工智能方法，k-最近邻，

结果

盲操作者评估根据发育成囊胚的能力对每个胚胎进行分类，准确率为 75.4%，敏感性为 76.5%，特异性为 74.3%，准确率为 74.3%，F1 分数为 75.4%。将人工智能模型的结果与盲算子分类的结果相结合，产生了 82.6% 的准确度、79.4% 的灵敏度、85.7% 的特异性、84.4% 的精确度和 81.8% 的 F1 分数。

结论

本研究提出了通过检测细胞质运动速度和人工智能分析来预测早期卵裂阶段人类囊胚发育的可能性。这表明细胞质动力学作为评估胚胎活力的新型有价值的数据来源的重要性。