AI-Driven Integrated System for Burn Depth Prediction With Electronic Medical Records: Algorithm Development and Validation
The storage of multimodal imaging data within the EMR, along with the ability for post hoc analysis by AI algorithms, offers significant advancements in burn care, enabling real-time burn depth prediction on currently available data. Using digital photos for superficial burns, easily diagnosed through physical examinations, reduces reliance on TDI, while TDI helps distinguish deep second- and third-degree burns, enhancing diagnostic efficiency.
Authors
- Gayle M. Gordillo (ORCID: https://orcid.org/0000-0001-8369-6637)
- Yexiang Xue (ORCID: https://orcid.org/0000-0002-4533-0543)
- Juan Wachs (ORCID: https://orcid.org/0000-0002-6425-5745)
- Mohamed S. El Masry (ORCID: https://orcid.org/0000-0002-2686-1509)
- Md Masudur Rahman (ORCID: https://orcid.org/0000-0002-3633-0621)
- Surya C. Gnyawali (ORCID: https://orcid.org/0000-0002-4214-8068)
Institutions
- University of Pittsburgh (US)
- Purdue University West Lafayette (US)
- McGowan Institute for Regenerative Medicine (US)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-08-15
- DOI
- https://doi.org/10.2196/68366
- Citations
- 1
- Primary Topic
- Fire Detection and Safety Systems
- Type
- article
- Field-Weighted Citation Impact
- 1.30
Funders
- National Institutes of Health