Point-of-Care Autofluorescence Imaging for Real-Time Sampling and Treatment Guidance of Bioburden in Chronic Wounds: First-in-Human Results
Bibliographic record
Abstract
BACKGROUND: Traditionally, chronic wound infection is diagnosed by visual inspection under white light and microbiological sampling, which are subjective and suboptimal, respectively, thereby delaying diagnosis and treatment. To address this, we developed a novel handheld, fluorescence imaging device (PRODIGI) that enables non-contact, real-time, high-resolution visualization and differentiation of key pathogenic bacteria through their endogenous autofluorescence, as well as connective tissues in wounds. METHODS AND FINDINGS: This was a two-part Phase I, single center, non-randomized trial of chronic wound patients (male and female, ≥18 years; UHN REB #09-0015-A for part 1; UHN REB #12-5003 for part 2; clinicaltrials.gov Identifier: NCT01378728 for part 1 and NCT01651845 for part 2). Part 1 (28 patients; 54% diabetic foot ulcers, 46% non-diabetic wounds) established the feasibility of autofluorescence imaging to accurately guide wound sampling, validated against blinded, gold standard swab-based microbiology. Part 2 (12 patients; 83.3% diabetic foot ulcers, 16.7% non-diabetic wounds) established the feasibility of autofluorescence imaging to guide wound treatment and quantitatively assess treatment response. We showed that PRODIGI can be used to guide and improve microbiological sampling and debridement of wounds in situ, enabling diagnosis, treatment guidance and response assessment in patients with chronic wounds. PRODIGI is safe, easy to use and integrates into the clinical workflow. Clinically significant bacterial burden can be detected in seconds, quantitatively tracked over days-to-months and their biodistribution mapped within the wound bed, periphery, and other remote areas. CONCLUSIONS: PRODIGI represents a technological advancement in wound sampling and treatment guidance for clinical wound care at the point-of-care. TRIAL REGISTRATION: ClinicalTrials.gov NCT01651845; ClinicalTrials.gov NCT01378728.
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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 | 0.000 | 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 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".