Introduction: There is significant inaccuracy and inconsistency associated with manual wound depth measurements1, typically achieved with a probe and ruler. This can inaccurately reflect a wound’s healing progress, significantly impacting treatment planning decisions. We sought to validate the accuracy, reproducibility, and usability of a novel automatic-depth feature (AutoDepth) on a hand-held wound imaging device* that also detects pathogenic bacteria both in a controlled environment and in an outpatient wound clinic.
Methods: A statistically powered validation study was performed using 17 three-dimensional wound models and 34 real clinical wounds. True depths were measured using a calibrated, highly accurate 3D scanner. Triplicate depth measurements of the wound models were captured by three users, and duplicate measurements of real clinical wounds were captured by two users. Testers were blinded to the true depths. Intraclass correlation coefficients (ICC) for intra- and inter-user variability were calculated using a 2-way random effects ANOVA model. In addition, 15 clinical wound measurement images were captured in an outpatient wound care clinic to assess the practicality and workflow impact of the AutoDepth feature.
Results: The automatic-depth feature was validated to accurately assess the wound depth with an error of ±0.87 mm for wound models and ±0.97 mm for real clinical wounds. Intra-user and inter-user ICCs were 0.999 (95% CI 0.997, 1) and 0.992 (95% CI 0.984, 0.996) respectively for wound models. Intra-user and inter-user ICCs were 0.998 (95% CI 0.996, 0.999) and 0.997 (95% CI 0.994, 0.998) respectively for real wounds assessed in the clinic. When implemented in fast-paced outpatient wound care clinic, AutoDepth proved effective and accurate, also streamlining the clinical workflow.
Discussion: AutoDepth measurements were highly accurate with excellent reproducibility in both benchtop and clinical testing. This novel feature within a multi-modal wound imaging platform* speeds up the clinical workflow, providing contactless, comprehensive digital wound measurements alongside co-registered bacterial fluorescence images.
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