Utility of Thermographic Imaging for Callus Identification in Wound and Foot Care
Bibliographic record
Abstract
Calluses are thickened skin areas that develop due to repeated friction, pressure, or other types of irritation. While calluses are usually harmless and formed as a protective surface, they can lead to skin ulceration or infection if left untreated. As calluses are often not clearly visible to the patients, and some areas of dead skin can be missed during debridement, accessory tools can be useful in assessment and follow-up. The practical question addressed in this article is whether or not thermal imaging adds value to callus assessment. We have performed a theoretical analysis of the feasibility of thermographic imaging for callus identification. Our analytical calculations show that the temperature drop in the epidermis should be on the order of 0.1 °C for the normal epidermis in hairy skin, 0.9 °C for glabrous skin, and 1.5-2 °C or higher in calluses. We have validated our predictions on gelatin phantoms and demonstrated the feasibility of thermographic imaging for callus identification in two clinical case series. Our experimental results are in agreement with theoretical predictions and support the notion that local skin temperature variations can indicate epidermis thickness variations, which can be used for callus identification. In particular, a surface temperature drop on the order of 0.5 °C or more can be indicative of callus presence, particularly in callus-prone areas. In addition, our analytical calculations and phantom experiments show the importance of ambient temperature measurements during thermographic assessments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".