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: 10675445
• 负责人: Peter James
• 金额: $ 75.74万
• 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/10675445
• 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; 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; residence; 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

项目成果

The PAD-US-AR dataset: Measuring accessible and recreational parks in the contiguous United States.

• DOI: 10.1038/s41597-022-01857-7
• 发表时间: 2022-12-16
• 期刊: SCIENTIFIC DATA
• 影响因子: 9.8
• 作者: Browning, Matthew H. E. M.;Rigolon, Alessandro;Ogletree, Scott;Wang, Ruoyu;Klompmaker, Jochem O. O.;Bailey, Christopher;Gagnon, Ryan;James, Peter
• 通讯作者: James, Peter

Associations of air pollution with COVID-19 positivity, hospitalisations, and mortality: Observational evidence from UK Biobank.

• DOI: 10.1016/j.envpol.2022.119686
• 发表时间: 2022-09-01
• 期刊: ENVIRONMENTAL POLLUTION
• 影响因子: 8.9
• 作者: Sheridan, Charlotte;Klompmaker, Jochem;Cummins, Steven;James, Peter;Fecht, Daniela;Roscoe, Charlotte
• 通讯作者: Roscoe, Charlotte

Nature's contributions in coping with a pandemic in the 21st century: A narrative review of evidence during COVID-19.

• DOI: 10.1016/j.scitotenv.2022.155095
• 发表时间: 2022-08-10
• 期刊: The Science of the total environment
• 作者: Labib SM;Browning MHEM;Rigolon A;Helbich M;James P
• 通讯作者: James P

Associations of fine particulate matter with incident cardiovascular disease; comparing models using ZIP code-level and individual-level fine particulate matter and confounders.

细颗粒物与心血管疾病的关联；

• DOI: 10.1016/j.scitotenv.2024.171866
• 发表时间: 2024
• 期刊: The Science of the total environment
• 作者: Klompmaker,JochemO;Hart,JaimeE;Dominici,Francesca;James,Peter;Roscoe,Charlie;Schwartz,Joel;Yanosky,JeffD;Zanobetti,Antonella;Laden,Francine
• 通讯作者: Laden,Francine

Light at night and the risk of breast cancer: Findings from the Sister study.

• DOI: 10.1016/j.envint.2022.107495
• 发表时间: 2022-11
• 期刊: ENVIRONMENT INTERNATIONAL
• 影响因子: 11.8
• 作者: Sweeney, Marina R.;Nichols, Hazel B.;Jones, Rena R.;Olshan, Andrew F.;Keil, Alexander P.;Engel, Lawrence S.;James, Peter;Jackson, Chandra L.;Sandler, Dale P.;White, Alexandra J.
• 通讯作者: White, Alexandra J.