Background and Aims Built environment plays an important role in the development of cardiovascular disease. Tools to evaluate the built environment using machine vision and informatic approaches have been limited. This study aimed to investigate the association between machine vision–based built environment and prevalence of cardiometabolic disease in US cities. Methods This cross-sectional study used features extracted from Google Street View (GSV) images to measure the built environment and link them with prevalence of coronary heart disease (CHD). Convolutional neural networks, linear mixed-effects models, and activation maps were utilized to predict health outcomes and identify feature associations with CHD at the census tract level. The study obtained 0.53 million GSV images covering 789 census tracts in seven US cities (Cleveland, OH; Fremont, CA; Kansas City, MO; Detroit, MI; Bellevue, WA; Brownsville, TX; and Denver, CO). Results Built environment features extracted from GSV using deep learning predicted 63% of the census tract variation in CHD prevalence. The addition of GSV features improved a model that only included census tract-level age, sex, race, income, and education or composite indices of social determinant of health. Activation maps from the features revealed a set of neighbourhood features represented by buildings and roads associated with CHD prevalence. Conclusions In this cross-sectional study, the prevalence of CHD was associated with built environment factors derived from GSV through deep learning analysis, independent of census tract demographics. Machine vision–enabled assessment of the built environment could potentially offer a more precise approach to identify at-risk neighbourhoods, thereby providing an efficient avenue to address and reduce cardiovascular health disparities in urban environments.

中文翻译：

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基于人工智能的谷歌街景建筑环境评估和冠状动脉疾病患病率

背景和目的建筑环境在心血管疾病的发生中起着重要作用。使用机器视觉和信息方法评估建筑环境的工具有限。本研究旨在调查美国城市中基于机器视觉的建筑环境与心脏代谢疾病患病率之间的关联。方法 这项横断面研究使用从谷歌街景 (GSV) 图像中提取的特征来测量建筑环境，并将其与冠心病 (CHD) 的患病率联系起来。利用卷积神经网络、线性混合效应模型和激活图来预测人口普查区水平的健康结果并确定与 CHD 的特征关联。该研究获得了 53 万张 GSV 图像，覆盖美国七个城市（俄亥俄州克利夫兰、加利福尼亚州弗里蒙特、密苏里州堪萨斯城、密歇根州底特律、华盛顿州贝尔维尤、德克萨斯州布朗斯维尔和科罗拉多州丹佛）的 789 个人口普查区。结果 使用深度学习从 GSV 中提取的构建环境特征预测了 63% 的人口普查区 CHD 患病率变化。 GSV 特征的添加改进了仅包括人口普查区域年龄、性别、种族、收入和教育或健康社会决定因素综合指数的模型。这些特征的激活图揭示了一组以与 CHD 患病率相关的建筑物和道路为代表的社区特征。结论 在这项横断面研究中，CHD 的患病率与通过深度学习分析从 GSV 得出的建筑环境因素相关，独立于人口普查区的人口统计数据。基于机器视觉的建筑环境评估可能会提供一种更精确的方法来识别高危社区，从而为解决和减少城市环境中的心血管健康差异提供有效的途径。