Design of A Smartphone Application for Automated Wound Measurements for Home Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: There are 8.5 million Americans who suffer from a chronic wound. Due to the lack of an objective system to measure and characterize wounds, the current standard of care relies highly on provider guesswork. This leads to misinformed care decisions, ultimately leading to prolonged healing times and high healthcare expenditures. Objective: This study describes the design and validation of a smartphone image-based system for accurately measuring and characterizing chronic wounds in an automated and objective fashion. Methods: Photos (n=81) were collected by the study team from patients (n=25) at the Johns Hopkins Bayview Wound Clinic in an IRB-approved study. Photos were taken using a variety of smartphones such that our training data set would include nuances of different smartphone cameras. We combined supervised image classification and computer vision to detect wound edges and segment the tissues within the wound. 15 individuals (“raters”) with various levels of training were then instructed to trace wound regions in a diverse subset of the wound images arbitrarily selected by the study team (n=10). The ensemble wound edge and tissue segmentation algorithms were compared against an 80% inter-rater gold standard. Results: The automated method resulted in a sensitivity = 98.31 ± 2.18 and specificity = 92.06 ± 7.86). In contrast, the ruler-based measurement resulted in sensitivity = 1 ± 0, Specificity = 0.57 ± 0.30. A normalized area measurement for the automated method resulted in a normalized area of 1.14 ± 0.17. In comparison, the standard of care method resulted in a normalized area of 1.86 ± 0.30 relative to gold standard. With respect to tissue segmentation, the overall average tissue classification accuracy on k-fold cross validation using the sparse neural network method is 93.6% ± 3.3%. Conclusions: The result illustrates the large overestimation of wound size that occurred when the wounds were measured using the ruler measurement. It also corroborates the literature-reported value of measurement inaccuracy by standard methods. Our study shows the feasibility of an easily deployed smartphone system to classify wounds in an automated manner with high accuracy. Such a system could be used to objectify measurements by nurses in the home care environment, thus improving the accuracy of wound care and, potentially, the outcomes of patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it