Built Environment Assessment through Computer visiON (BEACON): Applying Deep Learning to Street-Level and Satellite Images to Estimate Built Environment Effects on Cardiovascular Health

通过计算机视觉进行建筑环境评估 (BEACON)：将深度学习应用于街道和卫星图像，以估计建筑环境对心血管健康的影响

• 批准号: 10444927
• 负责人: Peter James
• 金额: $ 77.2万
• 依托单位: HARVARD PILGRIM HEALTH CARE, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10444927
• 关键词: Address; Adult; Air Pollution; Ancillary Study; Cardiovascular Diseases; Catalogs; Cellular Phone; Cessation of life; Cities; Computer Vision Systems; Coronary heart disease; Cross-Sectional Studies; Data; Environment; Environmental Hazards; Environmental Risk Factor; Exposure to; Follow-Up Studies; Global Positioning System; Green space; Health Professional; Health Promotion; Health Technology; Health behavior; Health behavior change; Image; Imagery; Incidence; Infrastructure; Intervention; Location; Measures; Methods; Myocardial Infarction; Noise; Nurses' Health Study; Outcome; Participant; Pathway interactions; Patient Self-Report; Pattern; Perception; Physical activity; Physical environment; Planet Earth; Policies; Population; Process; Prospective cohort; Prospective cohort study; Questionnaires; Recording of previous events; Resolution; Safety; Shapes; Specific qualifier value; Stroke; Time; Trees; Weight; Weight Gain; Woman; Work; base; built environment; cardiovascular health; cohort; deep learning; deep learning algorithm; design; experience; extreme temperature; follow-up; innovation; insight; land use; learning strategy; mHealth; machine learning algorithm; men; novel; petabyte; spatiotemporal; urban area; urban planning; wearable device

PROJECT SUMMARY Over 80% of the US population resides in urban areas, and the built environment—the buildings, streets, and green spaces in which we live—may drive cardiovascular disease (CVD) by promoting or limiting physical activity and weight gain, and by influencing exposures to environmental factors, such as air pollution, extreme temperatures, and noise. Evidence for the built environment and CVD has been dominated by cross-sectional studies with nonspecific exposure assessment. Developing precise, time-varying, and personalized exposure metrics is necessary to establish causal relationships between the built environment and CVD, which are crucial to informing policy-relevant, actionable interventions. It is now possible to estimate such exposure metrics at scale in prospective cohort studies using deep learning computer vision methods, a class of machine learning algorithms that can accurately process images, combined with time-varying nationwide street-level imagery, high resolution satellite data, and novel mobile health technologies. We propose to identify the influence of the built environment on CVD health behaviors and CVD incidence by developing built environment exposure measures from deep learning algorithms, and to apply these exposure measures to time-activity data in participants with global positioning systems (GPS) data from the Nurses’ Health Study 3 (N=500), and to geocoded residential addresses from nationwide Nurses’ Health Study, Nurses’ Health Study II, and Health Professionals Follow-up Study prospective cohorts (N=288,000). We will create built environment exposure measures by leveraging deep learning algorithms applied to nationwide Google Street View imagery (2007-2020) and high-resolution Landsat satellite data (1986-2020) to create fine-scale, time-varying built environment metrics of the natural environment (e.g., trees), physical environment (e.g., sidewalks), perceptions (e.g., safety), and urban form (e.g., compact high-rise). We will use a mix of innovative analytical approaches to determine the effect of the built environment on CVD-related health behaviors and CVD incidence across different time horizons. First, we will append these metrics to time-activity patterns of participants who have collected minute-level data on GPS and physical activity from smartphones and consumer wearable devices to quantify how minute-level exposure to the built environment is related to CVD health behaviors. Next, we will apply novel built environment metrics to residential address histories of participants to estimate how self-reported CVD health behaviors change after their residential built environment changes. Last, we will examine the association between long-term cumulative residential exposure to the built environment and CVD incidence over 34 years of follow-up. Our work will enable us to measure built environment exposure from unprecedented perspectives in large prospective cohorts, to elucidate potential causal relationships between the built environment and CVD health behaviors, and to better specify pathways to CVD incidence. Ultimately, our work will yield actionable insights to guide land use policy and urban planning strategies to design cities that optimize cardiovascular health.

项目总结 超过80%的美国人口居住在城市地区，建筑环境--建筑物、街道和 我们生活的绿地--可能通过促进或限制身体活动而引发心血管疾病(CVD 和体重增加，并通过影响暴露于环境因素，如空气污染，极端 温度和噪音。建筑环境和心血管疾病的证据主要是横断面 关于非特异性暴露评估的研究。开发精确、时变和个性化的曝光 为了建立建筑环境和心血管疾病之间的因果关系，衡量标准是必要的，这是至关重要的 提供与政策相关的、可操作的干预措施。现在可以估计这样的曝险指标为 使用深度学习计算机视觉方法的前瞻性队列研究的规模，机器学习的一类 能够准确处理图像的算法，结合时变的全国街道级图像，高 分辨率卫星数据，以及新的移动医疗技术。我们建议确定建造的影响 制定建筑环境暴露措施对心血管疾病健康行为和心血管疾病发病率的影响 来自深度学习算法，并将这些暴露措施应用于以下参与者的时间-活动数据 全球定位系统(GPS)数据，来自护士健康研究3(N=500)和地理编码住宅 来自全国护士健康研究、护士健康研究II和卫生专业人员后续行动的演讲 研究前瞻性队列(N=288,000)。我们将创建建筑环境暴露措施，通过利用深度 学习算法应用于全国范围内的谷歌街景图像(2007-2020年)和高分辨率陆地卫星 卫星数据(1986-2020)，以创建自然环境的精细、时变的建成环境指标 (例如，树木)、物理环境(例如，人行道)、感知(例如，安全性)和城市形态(例如，紧凑 高层建筑)。我们将使用多种创新的分析方法来确定建筑环境的影响。 不同时间段心血管疾病相关健康行为与心血管疾病发病率的关系。首先，我们将添加这些 已收集GPS和体力活动分钟级数据的参与者的时间活动模式的度量 从智能手机和消费者可穿戴设备来量化如何构建分钟级的 环境与心脑血管疾病的健康行为有关。接下来，我们将把新的建成环境指标应用于住宅 介绍参与者的历史记录，以评估自我报告的心血管疾病健康行为在 住宅建成环境发生变化。最后，我们将检验长期累积与 34年随访居民暴露于建成环境与心血管疾病发病率的关系。我们的工作将使 美国将从前所未有的角度在大型预期队列中衡量建筑环境风险敞口， 阐明建筑环境和心血管疾病健康行为之间的潜在因果关系，并更好地 具体说明心血管疾病的发病途径。最终，我们的工作将产生可操作的见解来指导土地使用政策 以及城市规划战略，以设计优化心血管健康的城市。