ImportanceBuilt environment plays an important role in development of cardiovascular disease. Large scale, pragmatic evaluation of built environment has been limited owing to scarce data and inconsistent data quality.ObjectiveTo investigate the association between image-based built environment and the prevalence of cardiometabolic disease in urban cities.Design, Setting, and ParticipantsThis cross-sectional study used features extracted from Google satellite images (GSI) to measure the built environment and link them with prevalence of cardiometabolic disease. Convolutional neural networks, light gradient-boosting machines, and activation maps were used to assess the association with health outcomes and identify feature associations with coronary heart disease (CHD), stroke, and chronic kidney disease (CKD). The study obtained aerial images from GSI covering census tracts in 7 cities (Cleveland, Ohio; Fremont, California; Kansas City, Missouri; Detroit, Michigan; Bellevue, Washington; Brownsville, Texas; and Denver, Colorado). The study used census tract-level data from the US Centers for Disease Control and Prevention’s 500 Cities project. The data were originally collected from the Behavioral Risk Factor Surveillance System that surveyed people 18 years and older across the country. Analyses were conducted from February to December 2022.ExposuresGSI images of built environment and cardiometabolic disease prevalence.Main Outcomes and MeasuresCensus tract-level estimated prevalence of CHD, stroke, and CKD based on image-based built environment features.ResultsThe study obtained 31 786 aerial images from GSI covering 789 census tracts. Built environment features extracted from GSI using machine learning were associated with prevalence of CHD (R2 = 0.60), stroke (R2 = 0.65), and CKD (R2 = 0.64). The model performed better at distinguishing differences between cardiometabolic prevalence between cities than within cities (eg, highest within-city R2 = 0.39 vs between-city R2 = 0.64 for CKD). Addition of GSI features both outperformed and improved the model that only included age, sex, race, income, education, and composite indices for social determinants of health (R2 = 0.83 vs R2 = 0.76 for CHD; P <.001). Activation maps from the features revealed certain health-related built environment such as roads, highways, and railroads and recreational facilities such as amusement parks, arenas, and baseball parks.Conclusions and RelevanceIn this cross-sectional study, a significant portion of cardiometabolic disease prevalence was associated with GSI-based built environment using convolutional neural networks.

中文翻译：

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基于深度学习的卫星图像建筑环境评估和心脏代谢疾病患病率

重要性建筑环境在心血管疾病的发生发展中起着重要作用。由于数据稀缺和数据质量不一致，大规模、务实的建筑环境评估受到限制。目的调查基于图像的建筑环境与城市中心脏代谢疾病患病率之间的关联。设计、设置和参与者这项横断面研究使用从谷歌卫星图像（GSI）中提取的特征来测量建筑环境并将其与心脏代谢疾病的患病率联系起来。卷积神经网络、光梯度增强机和激活图用于评估与健康结果的关联，并识别与冠心病 (CHD)、中风和慢性肾病 (CKD) 的特征关联。该研究从 GSI 获得了涵盖 7 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）人口普查区的航空图像。该研究使用了美国疾病控制与预防中心 500 个城市项目的人口普查区域数据。这些数据最初是从行为风险因素监测系统收集的，该系统对全国 18 岁及以上的人群进行了调查。分析于 2022 年 2 月至 12 月进行。暴露了建筑环境和心脏代谢疾病患病率的 GSI 图像。主要结果和措施根据基于图像的建筑环境特征估计了普查区一级的 CHD、中风和 CKD 患病率。结果该研究获得了 31 786 个航空数据来自 GSI 的图像涵盖 789 个人口普查区域。使用机器学习从 GSI 中提取的建筑环境特征与 CHD 的患病率相关（右2= 0.60), 行程 (右2= 0.65), 和 CKD (右2= 0.64）。该模型在区分城市之间心脏代谢患病率的差异方面比城市内部表现更好（例如，城市内最高右2= 0.39 vs 城市间右2= 0.64（对于 CKD）。添加 GSI 特征不仅优于并改进了仅包括年龄、性别、种族、收入、教育和健康社会决定因素综合指数的模型（右2= 0.83 对比右2冠心病 = 0.76；磷<.001)。这些特征的激活图揭示了某些与健康相关的建筑环境，如道路、高速公路和铁路，以及游乐园、竞技场和棒球场等娱乐设施。 结论和相关性在这项横断面研究中，很大一部分心脏代谢疾病患病率与使用卷积神经网络的基于 GSI 的构建环境相关联。