In Vitro Monitoring: The Next Revolution in IVF Technology

体外监测：IVF 技术的下一次革命

• 批准号: RGPIN-2018-05164
• 负责人: Saeedi, Parvaneh
• 金额: $ 2.4万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=684774
• 关键词: Vitro; Monitoring; Next; Revolution; IVF

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.

由于推迟生育，生育治疗继续大幅增加。不幸的是，女性的生殖能力从生命的第二和第三个十年开始下降，因此到40岁时，生育率显着降低，流产的可能性很高。辅助生殖技术包括涉及在体外处理卵子和胚胎的生育治疗。最著名的是体外受精，卵巢被过度刺激产生多个卵子用于外部受精。然后将受精卵（胚胎）孵育，直到它们达到胚泡阶段（受精后约5天），并选择用于植入。在孵育过程中，通过基于囊胚的内部形态结构对囊胚进行分级来鉴定最佳候选物。这个评估阶段是IVF治疗中的一个严格限制，可以通过使用工程科学的新技术进行革命。拟议的研究计划将研究信号处理，计算机视觉和机器学习策略，以挖掘与植入潜力相关的形态质量的时间流逝的显微胚胎图像。一个新的计算框架将开发基于分层架构-从单个帧到帧序列。在帧级别，形态特征和结构组件将通过使用模型和训练数据集的手工制作和深度学习算法自动识别。胚胎的结构成分是高度变异的，并表现出从一个阶段到另一个阶段的戏剧性转变，算法将相应地设计。在序列级别，统计模型和跟踪过滤器将从帧级别链接观察和计算的度量以创建累积分数。这将确定和量化胚胎发育的动态和形态动力学特征。** 通过这项拟议的研究计划，将首次有可能创造出在整个孵化过程中依赖于形态特征的临床适用的计算工具。这将使新的科学假设，以测试各种形态和形态动力学胚胎特征和妊娠结果之间的联系。