In Vitro Monitoring: The Next Revolution in IVF Technology

由于推迟生育，生育治疗继续急剧增加。不幸的是，女性的生育能力从20岁到30岁开始下降，到40岁时，生育能力显著下降，流产的几率很高。辅助生殖技术包括在体外处理卵子和胚胎的生育治疗。最著名的一种是体外受精，通过过度刺激卵巢产生多个卵子进行体外受精。然后将受精卵（胚胎）孵育至囊胚期（受精后约5天）并选择植入。在孵化过程中，根据囊胚的内部形态结构对其进行分级，从而确定最佳候选囊胚。这个评估阶段是体外受精治疗的限制，可以通过使用工程科学的新技术来彻底改变。

Fertility treatment has continued to increase dramatically as a result of delayed childbearing. Unfortunately, female reproductive capacity declines from the 2nd and 3rd decades of life so that by the age of 40 there is a significant reduction in fertility with a high chance of miscarriage. Assisted Reproductive Technology includes fertility treatments that involve handling eggs and embryos outside the body. The best known one is In Vitro Fertilization where the ovaries are hyper-stimulated to produce multiple eggs for external fertilization. The fertilized eggs (embryos) are then incubated until they reach the blastocyst stage (about 5 days after fertilization) and selected for implantation. Optimal candidates are identified during the incubation process by grading the blastocysts based on their internal morphological structures. This evaluation stage is the biting constraint in IVF treatment and can be revolutionized though the use of new technology from engineering science. The proposed research programme will investigate signal processing, computer vision, and machine learning strategies to mine time-lapsed microscopic embryo images for morphological qualities related to implantation potential. A new computational framework will be developed based on a hierarchical architecture - from single frames to frame sequences. At the frame level, morphological features and structural components will be identified automatically through hand-crafted and deep learning algorithms using models and training datasets. The structural components of an embryo are highly variant and exhibit dramatic transitions from one phase to another, and the algorithms will be designed accordingly. At the sequence level, statistical models and tracking filters will link the observations and computed metrics from the frame level to create cumulative scores. This will identify and quantify the dynamic and morpho-kinetic characteristics of the embryonic developments. Creating clinically applicable computational tools that rely on morphological features throughout the incubation will be made possible for the first time through this proposed research programme. This will enable new scientific hypotheses to be tested about the link between various morphologic and morpho-kinetic embryo characteristics and pregnancy outcomes.