Deep learning analysis of Google Street View to assess residential built environment and cardiovascular risk in a Midwestern US retrospective cohort
Visible vertical greenspace and sidewalks, as discerned from street-level images using deep learning, demonstrated potential associations with cardiovascular risk. This innovative approach highlights the potential of deep learning to analyze built environments at scale, offering new avenues for public health research. Future research is needed to validate these associations and better understand the underlying mechanisms.
Authors
- Jean‐Eudes Dazard (ORCID: https://orcid.org/0000-0003-4720-3684)
- Pedro Rafael Vieira de Oliveira Salerno (ORCID: https://orcid.org/0000-0002-5116-5062)
- Sadeer G Al-Kindi (ORCID: https://orcid.org/0000-0002-1122-7695)
- Zhuo Chen (ORCID: https://orcid.org/0000-0001-7781-2080)
- Salil V. Deo (ORCID: https://orcid.org/0000-0002-4729-1461)
- Mohamed H. E. Makhlouf (ORCID: https://orcid.org/0000-0002-3592-2844)
- Sanjay Rajagopalan
Institutions
- Houston Methodist (US)
- University Hospitals of Cleveland (US)
- Methodist Hospital (US)
- Louis Stokes Cleveland VA Medical Center (US)
- Case Western Reserve University (US)
Publication Details
- Journal
- European Journal of Preventive Cardiology
- Published
- 2025-02-04
- DOI
- https://doi.org/10.1093/eurjpc/zwaf038
- Citations
- 7
- Primary Topic
- Air Quality and Health Impacts
- Type
- article
- Field-Weighted Citation Impact
- 8.40