Chen Zhuo, Khalifa Yassin, Dazard Jean-Eudes, Motairek Issam, Rajagopalan Sanjay, Al-Kindi Sadeer
Harrington Heart and Vascular Institute, University Hospitals, and School of Medicine, Case Western Reserve University, Cleveland, OH.
medRxiv. 2023 Mar 29:2023.03.28.23287888. doi: 10.1101/2023.03.28.23287888.
Built environment plays an important role in development of cardiovascular disease. Tools to evaluate the built environment using machine vision and informatic approaches has been limited. We sought to investigate the association between machine vision-based built environment and prevalence of cardiometabolic disease in urban cities.
This cross-sectional study used features extracted from Google Street view (GSV) images to measure the built environment and link them with prevalence of cardiometabolic disease. Convolutional neural networks, light gradient boosting machines and activation maps were utilized to predict health outcomes and identify feature associations with coronary heart disease (CHD). The study obtained 0.53 million GSV images covering 789 census tracts in 7 cities (Cleveland, OH; Fremont, CA; Kansas City, MO; Detroit, MI; Bellevue, WA; Brownsville, TX; and Denver, CO). Analyses were conducted from February 2022 to December 2022. We used census tract-level data from the Centers for Disease Control and Prevention's PLACES dataset. Main outcomes included census tract-level estimated prevalence of CHD based on GSV built environment features.
Built environment features extracted from GSV using deep learning predicted 63% of the census tract variation in CHD prevalence. The ExtraTrees Regressor achieved the best result among all models with the lowest average mean absolute error of 1.11% and Root mean square of error of 1.58. The addition of GSV features outperformed and improved a model that only included census-tract level age, sex, race, income and education. Activation maps from the features revealed a set of neighborhood features represented by buildings and roads associated with CHD prevalence.
In this cross-sectional study, a significant portion of CHD prevalence were explained by GSV-based built environment factors analyzed using deep learning, independent of census tract demographics. Machine vision enabled assessment of the built environment could help play a significant role in designing and improving heart-heathy cities.
建筑环境在心血管疾病的发展中起着重要作用。利用机器视觉和信息学方法评估建筑环境的工具一直很有限。我们试图研究基于机器视觉的建筑环境与城市中心血管代谢疾病患病率之间的关联。
这项横断面研究利用从谷歌街景（GSV）图像中提取的特征来测量建筑环境，并将其与心血管代谢疾病的患病率联系起来。利用卷积神经网络、轻梯度提升机器和激活图来预测健康结果，并识别与冠心病（CHD）的特征关联。该研究获得了53万张GSV图像，覆盖了7个城市（俄亥俄州克利夫兰市；加利福尼亚州弗里蒙特市；密苏里州堪萨斯城；密歇根州底特律市；华盛顿州贝尔维尤市；得克萨斯州布朗斯维尔市；科罗拉多州丹佛市）的789个人口普查区。分析于2022年2月至2022年12月进行。我们使用了疾病控制和预防中心的PLACES数据集中的人口普查区层面的数据。主要结果包括基于GSV建筑环境特征的人口普查区层面的冠心病估计患病率。
使用深度学习从GSV中提取的建筑环境特征预测了冠心病患病率中63%的人口普查区差异。在所有模型中，ExtraTrees回归器取得了最佳结果，平均平均绝对误差最低，为1.11%，均方根误差为1.58。GSV特征的加入优于并改进了一个仅包括人口普查区层面的年龄、性别、种族、收入和教育程度的模型。这些特征的激活图揭示了一组由与冠心病患病率相关的建筑物和道路所代表的邻里特征。
在这项横断面研究中，使用深度学习分析的基于GSV的建筑环境因素解释了很大一部分冠心病患病率，独立于人口普查区的人口统计学特征。利用机器视觉对建筑环境进行评估有助于在设计和改善心脏健康城市方面发挥重要作用。
