Towards algorithm‐enabled home wound monitoring with smartphone photography: A hue‐saturation‐value colour space thresholding technique for wound content tracking
Bibliographic record
Abstract
Automated tracking of wound-healing progress using images from smartphones can be useful and convenient for the patient to perform at home. To evaluate the feasibility, 119 images were taken with an iPhone smartphone during the treatment of a chronic wound at one patient's home. An image analysis algorithm was developed to quantitatively classify wound content as an index of wound healing. The core of the algorithm involves transforming the colour image into hue-saturation-value colour space, after which a threshold can be reliably applied to produce segmentation using the Black-Yellow-Red wound model. Morphological transforms are used to refine the classification. This method was found to be accurate and robust with respect to lighting conditions for smartphone-captured photos. The wound composition percentage showed a different trend from the wound area measurements, suggesting its role as a complementary metric. Overall, smartphone photography and automated image analysis is a promising cost-effective way of monitoring patients. While the current setup limits our capability of measuring wound area, future smartphones equipped with depth-sensing technology will enable accurate volumetric evaluation in addition to composition analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".