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

• 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
• 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

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）是想要实现一个不育夫妇的临床治疗 怀孕。在艺术中​​，胚胎学家从患者或捐赠者中获取卵，培养产生的胚胎 在体外，然后将选定的胚胎转移到母亲的子宫中。虽然艺术负责1.9％的婴儿 截至2018年，出生于美国，选择要转移的胚胎是一个重要的挑战。困难 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。由于这种困难，经常转移多个胚胎以提高 成功，导致多重怀孕率接近20％，这可能导致显着的发病率和医疗 给病人的费用。理想是仅转移一个胚胎，但这是必要的 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以改善胚胎 艺术选择。 为此，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，用于人类融合分析。我们将首先提取并分析 常规收集的胚胎图像。然后，我们将这些视觉特征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，这些模型在其可行性上评分胚胎。我们计划 将我们的机器学习解决方案集成到一个易于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颖的基于机器学习的模型来实现形态特征提取物 - 每个混杂因素和艺术临床决策支持系统的估计和重要性估计。为了 形态学特征提取，我们计划对卷积神经网络进行半监督学习 最大程度地减少需要大量人为努力的手动标签。我们的功能提取模型将是第一个 综合艺术分类和分割方法。为了帮助选择胚胎，我们将发展 新颖的基于学习的新型模型，以预测通过接受视觉特征和 EHR数据作为输入。我们还将开发视觉分析工具，使分析师能够更好地理解和启动 这些深度学习模型。我们将估算每个输入因素在胚胎选择中的重要性 解释对胚胎学家的预测。最后，我们将开发Embryopro -Filler，这是一种临床决策支持 艺术系统，将我们的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。胚胎填充器将帮助支持日常操作 诊所，促进人类引导的决策，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以提高艺术成功率。我们的项目将 为艺术临床医生创建最先进的分析方法。