AI-Enhanced Analysis of Built Environment Imagery and Neighborhood Obesity in US Cities
In this study, integrating AI-derived built environment features from geospatial imagery was associated with enhanced ability to explain and estimate neighborhood-level obesity prevalence beyond conventional DSE and SDOH factors.
Authors
- Jean‐Eudes Dazard (ORCID: https://orcid.org/0000-0003-4720-3684)
- Sanjay Rajagopalan (ORCID: https://orcid.org/0000-0001-6669-8163)
- 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)
- Skanda Moorthy (ORCID: https://orcid.org/0000-0001-9861-8834)
- Haitham Khraishah (ORCID: https://orcid.org/0000-0003-0003-5089)
- Weichuan Dong (ORCID: https://orcid.org/0000-0002-3981-658X)
- Santosh Kumar Sirasapalli (ORCID: https://orcid.org/0009-0003-9373-8310)
- Tong Zhang (ORCID: https://orcid.org/0000-0002-5725-9510)
- Sai Rahul Ponnana
Institutions
- Houston Methodist (US)
- University Hospitals of Cleveland (US)
- Case Western Reserve University (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2025-09-30
- DOI
- https://doi.org/10.1001/jamanetworkopen.2025.34612
- Citations
- 1
- Primary Topic
- Impact of Light on Environment and Health
- Type
- article
- Field-Weighted Citation Impact
- 1.11
Funders
- National Institutes of Health