Enhancing Assisted Reproductive Technologies with Deep Learning and Data Visualization

通过深度学习和数据可视化增强辅助生殖技术

基本信息

批准号：
10376335
负责人：
Dalit Ben Yosef
金额：
$ 68.39万
依托单位：
HARVARD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2026-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10376335
关键词：
Adopted Age Assisted Reproductive Technology Back Cell Lineage Classification Clinic Clinical Clinical Data Clinical Decision Support Systems Clinical Treatment Cloud Service Communities Complex Computer Models Computer Vision Systems Computers Couples Data Data Analyses Data Set Data Storage and Retrieval Databases Decision Making Detection Development Discipline E-learning Electronic Health Record Embryo Embryo Transfer Embryonic Development Fostering Goals Human Human Biology Image Image Analysis In Vitro Judgment Knowledge Label Lead Machine Learning Manuals Medical Methods Modeling Morbidity - disease rate Morphology Mothers Multiple Pregnancy Obesity Patients Pattern Physiological Pre-implantation Embryo Development Pregnancy Pregnancy Rate Privacy Probability Research Science Scientist Secure Security Text Time Trees United States Ursidae Family Uterus Visual Visualization Visualization software analytical tool base blastocyst clinical decision-making clinical practice cloud based cohort convolutional neural network data cleaning data curation data management data visualization deep learning deep learning model embryo cell embryo monitoring feature extraction human-in-the-loop implantation improved infertility treatment insight large scale data machine learning algorithm machine learning model microscopic imaging model design multi-task learning multimodality novel operation predictive modeling success supervised learning tool unsupervised learning zygote

项目摘要

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.

项目摘要辅助繁殖技术（ART）是想要实现的不育夫妇的临床宝藏怀孕。在体外，以及母亲的子宫的选定胚胎。截至2018年，出生于美国，选择哪种胚胎转移是一个重要的挑战。来自混杂因素的复杂性以及对人类前植入前的缺乏理解胚胎发育。成功，导致多个怀孕率接近20％，这可能导致病态和医疗给患者的费用。理想是转移一个胚胎我们的队列最佳胚胎。我们建议创建一个临床决策支持系统以改善胚胎艺术选择。这一目的，我们将开发出新颖的深度学习模型，用于鲁棒的胚胎特征提取和交互式数据可视化方法用于循环分析。常规收集的胚胎图像。记录（EHR）的数据，以开发可解释的计算模型，以我们计划在其生存力上进行评分将我们的机器学习解决方案集成到一个容易访问的云服务平台中，该平台将具有适应性在整个诊所中，以改善ART胚胎的选择和临床数据分析。我们的研究目标将通过新型的基于机器学习的形态模型来实现 - 对每个混杂因素和艺术临床决策系统的意义和重要性估计。形态学特征提取，我们计划进行传统网络的半监督为了最大程度地减少需要大量人类努力的手动标签。全面的分类和分割方法，以帮助胚胎选择新颖的基于学习的模型，通过接受视觉特征和 EHR数据作为输入。这些深度学习模型。解释胚胎学的预测。艺术系统，将我们的基于机器学习的模型与面向用户的Visuallytic套件相结合支持用户指导和临床决策的工具诊所，促进人类指导的决策，丰富数据驱动的胚胎分析，并增强能力选择最有能力的胚胎来转移以提高我们的项目为艺术临床医生创建最先进的分析方法。