Chen Zhuo, Salerno Pedro R V O, Dazard Jean-Eudes, Makhlouf Mohamed He, Deo Salil, Rajagopalan Sanjay, Al-Kindi Sadeer
Harrington Heart and Vascular Institute, University Hospitals, and School of Medicine, Case Western Reserve University, Cleveland, OH.
Center for Health and Nature and Department of Cardiology, Houston Methodist, Houston, TX.
Eur J Prev Cardiol. 2025 Feb 4. doi: 10.1093/eurjpc/zwaf038.
Cardiovascular disease (CVD) is a leading global cause of mortality. Environmental factors are increasingly recognized as influential determinants of cardiovascular health. Nevertheless, a finer-grained understanding of the effects of the built environment remains crucial for comprehending CVD. We sought to investigate the relationship between built environment features, including residential greenspace and sidewalks, and cardiovascular risk using street-level imagery and deep learning techniques.
This study employed Google Street View (GSV) imagery and deep learning techniques to analyze built environment features around residences in relation to major adverse cardiovascular events (MACE) risk. Data from a Northeast Ohio cohort were utilized. Various covariates, including socioeconomic and environmental factors, were incorporated in Cox Proportional Hazards models.
Of 49,887 individuals included, 2,083 experienced MACE over a median follow-up of 26.86 months. Higher tree-sky index and sidewalk presence were associated with reduced MACE risk (HR: 0.95, 95% CI: 0.91-0.99, and HR: 0.91, 95% CI: 0.87-0.96, respectively), even after adjusting for demographic, socioeconomic, environmental, and clinical factors.
Visible vertical greenspace and sidewalks, as discerned from street-level images using deep learning, demonstrated potential associations with cardiovascular risk. This innovative approach highlights the potential of deep learning to analyze built environments at scale, offering new avenues for public health research. Future research is needed to validate these associations and better understand the underlying mechanisms.
心血管疾病（CVD）是全球主要的死亡原因。环境因素日益被认为是心血管健康的重要决定因素。然而，对建筑环境影响的更细致理解对于理解CVD仍然至关重要。我们试图利用街景图像和深度学习技术研究包括住宅绿地和人行道在内的建筑环境特征与心血管风险之间的关系。
本研究采用谷歌街景（GSV）图像和深度学习技术，分析住宅周围的建筑环境特征与主要不良心血管事件（MACE）风险的关系。利用了俄亥俄州东北部队列的数据。各种协变量，包括社会经济和环境因素，被纳入Cox比例风险模型。
在纳入的49887名个体中，2083人在中位随访26.86个月期间发生了MACE。即使在调整了人口统计学、社会经济、环境和临床因素后，较高的树-天空指数和人行道的存在与降低的MACE风险相关（风险比分别为：0.95，95%置信区间：0.91-0.99，以及风险比：0.91，95%置信区间：0.87-0.96）。
通过深度学习从街景图像中识别出的可见垂直绿地和人行道，显示出与心血管风险的潜在关联。这种创新方法突出了深度学习大规模分析建筑环境的潜力，为公共卫生研究提供了新途径。需要进一步的研究来验证这些关联并更好地理解潜在机制。
