Satellite images and machine learning can identify remote communities to facilitate access to health services. (14th August 2019)

• Record Type: Journal Article
• Authors: Bruzelius, Emilie; Le, Matthew; Kenny, Avi; Downey, Jordan; Danieletto, Matteo; Baum, Aaron; Doupe, Patrick; Silva, Bruno; Landrigan, Philip J; Singh, Prabhjot
• Is Part Of: Journal of the American Medical Informatics Association. Volume 26:Number 8/9(2019)
• Page Start: 806
• Page End: 812
• Publication Date: 2019-08-14
• Subjects: global health -- public health surveillance -- community health workers -- deep learning -- neural networks; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocz111
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Community health systems operating in remote areas require accurate information about where people live to efficiently provide services across large regions. We sought to determine whether a machine learning analyses of satellite imagery can be used to map remote communities to facilitate service delivery and planning. Materials and Methods: We developed a method for mapping communities using a deep learning approach that excels at detecting objects within images. We trained an algorithm to detect individual buildings, then examined building clusters to identify groupings suggestive of communities. The approach was validated in southeastern Liberia, by comparing algorithmically generated results with community location data collected manually by enumerators and community health workers. Results: The deep learning approach achieved 86.47% positive predictive value and 79.49% sensitivity with respect to individual building detection. The approach identified 75.67% (n = 451) of communities registered through the community enumeration process, and identified an additional 167 potential communities not previously registered. Several instances of false positives and false negatives were identified. Discussion: Analysis of satellite images is a promising solution for mapping remote communities rapidly, and with relatively low costs. Further research is needed to determine whether the communities identified algorithmically, but not registered in the manual enumeration process, are currently inhabited. Conclusions: To our knowledge, this study represents the first effort to apply image recognition algorithms to rural healthcare delivery. Results suggest that these methods have the potential to enhance community health worker scale-up efforts in underserved remote communities.