Dahu Butros M, Khan Solaiman, Toubal Imad Eddine, Alshehri Mariam, Martinez-Villar Carlos I, Ogundele Olabode B, Sheets Lincoln R, Scott Grant J
University of Missouri, Institute for Data Science and Informatics, Columbia, MO, United States.
JMIR AI. 2024 Dec 17;3:e64362. doi: 10.2196/64362.
The global obesity epidemic demands innovative approaches to understand its complex environmental and social determinants. Spatial technologies, such as geographic information systems, remote sensing, and spatial machine learning, offer new insights into this health issue. This study uses deep learning and spatial modeling to predict obesity rates for census tracts in Missouri.
This study aims to develop a scalable method for predicting obesity prevalence using deep convolutional neural networks applied to satellite imagery and geospatial analysis, focusing on 1052 census tracts in Missouri.
Our analysis followed 3 steps. First, Sentinel-2 satellite images were processed using the Residual Network-50 model to extract environmental features from 63,592 image chips (224×224 pixels). Second, these features were merged with obesity rate data from the Centers for Disease Control and Prevention for Missouri census tracts. Third, a spatial lag model was used to predict obesity rates and analyze the association between deep neural visual features and obesity prevalence. Spatial autocorrelation was used to identify clusters of obesity rates.
Substantial spatial clustering of obesity rates was found across Missouri, with a Moran I value of 0.68, indicating similar obesity rates among neighboring census tracts. The spatial lag model demonstrated strong predictive performance, with an R of 0.93 and a spatial pseudo R of 0.92, explaining 93% of the variation in obesity rates. Local indicators from a spatial association analysis revealed regions with distinct high and low clusters of obesity, which were visualized through choropleth maps.
This study highlights the effectiveness of integrating deep convolutional neural networks and spatial modeling to predict obesity prevalence based on environmental features from satellite imagery. The model's high accuracy and ability to capture spatial patterns offer valuable insights for public health interventions. Future work should expand the geographical scope and include socioeconomic data to further refine the model for broader applications in obesity research.
全球肥胖流行需要创新方法来理解其复杂的环境和社会决定因素。地理信息系统、遥感和空间机器学习等空间技术为这一健康问题提供了新的见解。本研究使用深度学习和空间建模来预测密苏里州普查区的肥胖率。
本研究旨在开发一种可扩展的方法，使用应用于卫星图像和地理空间分析的深度卷积神经网络来预测肥胖患病率，重点关注密苏里州的1052个普查区。
我们的分析遵循三个步骤。首先，使用残差网络-50模型处理哨兵-2卫星图像，从63592个图像块（224×224像素）中提取环境特征。其次，将这些特征与疾病控制和预防中心提供的密苏里州普查区肥胖率数据合并。第三，使用空间滞后模型预测肥胖率，并分析深度神经视觉特征与肥胖患病率之间的关联。使用空间自相关来识别肥胖率的聚类。
在密苏里州发现了肥胖率的大量空间聚类，莫兰I值为0.68，表明相邻普查区的肥胖率相似。空间滞后模型显示出强大的预测性能，R值为0.93，空间伪R值为0.92，解释了肥胖率变化的93%。空间关联分析的局部指标揭示了肥胖率明显高和低聚类的区域，这些区域通过分级统计图进行了可视化。
本研究强调了整合深度卷积神经网络和空间建模以基于卫星图像的环境特征预测肥胖患病率的有效性。该模型的高精度和捕捉空间模式的能力为公共卫生干预提供了有价值的见解。未来的工作应扩大地理范围并纳入社会经济数据，以进一步完善模型，使其在肥胖研究中得到更广泛的应用。
