Temporal machine learning framework for diabetic foot ulcer healing trajectory prediction
This framework demonstrates potential for next-appointment trajectory prediction using accessible clinical metadata without specialized imaging, pending prospective validation. The chronicity-dependent recommendation performance appropriately distinguishes wounds amenable to standardized protocols from treatment-resistant cases requiring iterative experimentation.
Authors
- Reza Basiri (ORCID: https://orcid.org/0000-0002-0209-6478)
- Shehroz S. Khan (ORCID: https://orcid.org/0000-0002-1195-4999)
- Asem Saleh (ORCID: https://orcid.org/0000-0002-2577-6054)
- M. Popovic
Institutions
- University of Toronto (CA)
- Toronto Rehabilitation Institute (CA)
- American University of the Middle East (KW)
Publication Details
- Journal
- BioMedical Engineering OnLine
- Published
- 2026-02-05
- DOI
- https://doi.org/10.1186/s12938-026-01529-2
- Primary Topic
- Diabetic Foot Ulcer Assessment and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00