Accessible healing phase classification of diabetic foot ulcer
Bibliographic record
Abstract
OBJECTIVE: Diabetic foot ulcers (DFU) are complex wounds that, without proper treatment, can lead to leg amputation. Treatment of a DFU is multifaceted and requires a high level of clinical expertise. This study aims to inform medical triage and improve healing rates by developing accessible and automated assessment and classification of wound healing phases and analyzing and identifying essential clinical and wound features for the classification. APPROACH: Machine learning models were evaluated using the Zivot DFU dataset to classify patients' wound healing phases from inflammation, proliferation, and remodeling classes. The models were trained on clinical features from 268 unique patients and 890 data points, including 80 features describing patient demographics, comorbidities, and wound characteristics. The area under the receiver operating characteristic curve, accuracy, and F1 values were used to assess the models' performances, and Shapley Additive Explanations were used to analyze the importance of features. RESULTS: 56 of the 80 features provided an accuracy of 65 %, while 22 essential features were enough to achieve a lower but statistically similar accuracy with an ordinal three-class classification using random forest classifiers. INNOVATION: This study provides a novel approach to classifying the wound-healing phase of a DFU based on key features available from wound- and patient-level metadata. CONCLUSION: The accessible and automated machine learning approach's ease of use and reliability will promote early and continuous autonomous medical triaging, ultimately improving patient outcomes. Additionally, the identified essential clinical features correlating with healing phases provide insight into DFU data management.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".