Enhancing Assisted Reproductive Technologies with Deep Learning and Data Visualization

项目总结 辅助生殖技术(ART)是一种临床治疗方法，适用于那些想要获得 怀孕了。在ART中，胚胎学家使从患者或捐赠者那里取出的卵子受精，培养产生的胚胎。 在体外，然后将选定的胚胎(S)移植到母亲的子宫中。而抗逆转录病毒治疗对1.9%的婴儿负有责任 截至2018年出生在美国，选择移植哪个胚胎是一个重大的fi挑战。Diffi邪教 源于混杂因素的复杂性和对人类植入前的认识不足 胚胎发育。由于这种差异，多个胚胎经常被移植以增加fi的潜力 成功，导致多胎妊娠率接近20%，这可能导致显著的fi发病率和医疗 病人的费用。理想的做法是只移植一个胚胎，但这需要有能力选择 队列中最好的胚胎。在这里，我们建议创建一个临床决策支持系统来改善胚胎 艺术精选。 为此，我们将开发新的深度学习模型，用于稳健的胚胎特征提取和交互 用于人在环中分析的数据可视化方法。我们将首先从fi提取和分析视觉特征 例行公事地收集胚胎图像。然后，我们将把这些视觉功能与患者的电子健康相结合 记录(EHR)数据，以开发可解释的计算模型，根据胚胎的生存能力对其进行评分。我们计划 将我们的机器学习解决方案集成到易于访问的云服务平台中，该平台将具有适应性 跨临床完善ART胚胎选择和临床数据分析。 我们的研究目标将通过新的基于机器学习的形态特征提取模型来实现。 每个混杂因素的预测和重要性评估以及ART的临床决策支持系统。为 形态特征提取，我们计划进行卷积神经网络的半监督学习 以最大限度地减少需要大量人力的手动标记。我们的特征提取模型将是fiRst 针对ART的综合经典fi拼接和分割方法。为了帮助胚胎选择，我们将开发 新的基于深度学习的模型通过接受视觉特征和 以电子病历数据作为输入。我们还将开发可视化分析工具，使分析师能够更好地理解和指导 这些深度学习模型。我们将评估每个输入解释因素在胚胎选择中的重要性。 向胚胎学家解释这一预测。最后，我们将开发一个临床决策支持系统EmbryoProfiLer ART系统，它将我们基于机器学习的模型与面向用户的视觉分析套件相结合 支持用户指导和临床决策的工具。EmbryoProfiLer将帮助简化 诊所，培养以人为本的决策制定，丰富数据驱动的胚胎分析，并增强 选择发育能力最强的胚胎进行移植，以提高ART成功率。我们的项目将 为临床医生创建最先进的分析方法。

PROJECT SUMMARY Assisted Reproduction Technology (ART) is a clinical treatment for infertile couples who want to achieve a pregnancy. In ART, embryologists fertilize eggs retrieved from the patient or a donor, culture the resulting embryos in vitro, and then transfer the selected embryo(s) to the mother's uterus. While ART is responsible for 1.9% of babies born in the United States as of 2018, selecting which embryo to transfer is a significant challenge. The difficulty comes from the complexity of confounding factors and the lack of understanding of human pre-implantation embryo development. Because of this difficulty, multiple embryos are often transferred to increases the potential of success, resulting in multiple pregnancy rates of nearly 20%, which can lead to significant morbidity and medical expenses to patients. The ideal is to transfer only a single embryo, but this necessitates the ability to select the best embryo from a cohort. Here, we propose to create a clinical decision support system to improve embryo selection in ART. To this end, we will develop novel deep learning models for robust embryo feature extraction and interactive data visualization methods for human-in-the-loop analysis. We will first extract and analyze visual features from routinely collected images of embryos. We will then combine these visual features with patients' electronic health record (EHR) data to develop interpretable computation models that score embryos on their viability. We plan to integrate our machine learning solutions into an easily accessible cloud service platform that will be adaptable across clinics to improve ART embryo selection and clinical data analysis. Our research goals will be achieved by novel machine learning-based models for morphological feature extrac- tion and importance estimation of each confounding factor and a clinical decision support system for ART. For morphological feature extraction, we plan to conduct semi-supervised learning of convolutional neural networks to minimize manual labeling that requires extensive human effort. Our feature extraction model will be the first comprehensive classification and segmentation method for ART. To aid in embryo selection, we will develop novel deep learning-based models to predict probabilities of achieving pregnancy by accepting visual features and EHR data as the input. We will also develop visual analytic tools that allow analysts to better understand and steer these deep learning models. We will estimate the importance of each input interpretable factor in embryo selection to explain the prediction to embryologists. Finally, we will develop EmbryoProfiler, a clinical decision support system for ART, that combines our machine learning-based models with a user-facing suite of visual analytic tools to support user guidance and clinical decision making. EmbryoProfiler will help facilitate daily operation in clinics, foster human-guided decision making, enrich data-driven embryo analysis, and enhance the ability to select the developmentally most competent embryo for transfer to improve ART success rates. Our project will create state-of-the-art analysis approaches for ART clinicians.