A Comprehensive Scoping Review on the Use of Point‐Of‐Care Infrared Thermography Devices for Assessing Various Wound Types
Bibliographic record
Abstract
This scoping review investigates the use of point-of-care infrared thermography devices for assessing various wound types. A comprehensive search across four databases yielded 76 studies published between 2010 and 2024 that met the inclusion criteria. The review highlights thermography applications in burns, surgical wounds, diabetic foot ulcers, pressure injuries, and other lower limb wounds. Key findings indicate its effectiveness in detecting early signs of inflammation and healing delays, facilitating timely interventions. The technology shows promise in accurately predicting wound healing trajectories and assessing treatment outcomes. Recent advancements have made thermographic devices more affordable and user-friendly, expanding their clinical potential. However, challenges persist, including reimbursement, training requirements, and integration with electronic medical records (EMRs), with EMR integration identified as a critical barrier to widespread adoption. While preliminary findings are promising, the current evidence base is constrained by small sample sizes, retrospective study designs, and limited consideration of skin tone variability. Large, prospective studies are essential to validate the clinical utility of thermography in wound care and to inform the development of standardised protocols that support equitable, bias-reduced assessment across diverse populations. Addressing these gaps is critical for advancing research, enhancing clinician training, and improving patient outcomes in wound care. Overall, point-of-care thermography demonstrates significant potential to enhance wound assessment and monitoring, thereby elevating care quality and patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".