Abstract
Accurate assessment of wound area and percentage of granulation tissue (PGT) are important for optimizing wound care and healing outcomes. Artificial intelligence (AI)-based wound assessment tools have the potential to improve the accuracy and consistency of wound area and PGT measurement, while improving efficiency of wound care workflows. To develop a quantitative and qualitative method to evaluate AI-based wound assessment tools compared with expert human assessments. This diagnostic study was performed across 2 independent wound centers using deidentified wound photographs collected for routine care (site 1, 110 photographs taken between May 1 and 31, 2018; site 2, 89 photographs taken between January 1 and December 31, 2019). Digital wound photographs of patients were selected chronologically from the electronic medical records from the general population of patients visiting the wound centers. For inclusion in the study, the complete wound edge and a ruler were required to be visible; circumferential ulcers were specifically excluded. Four wound specialists (2 per site) and an AI-based wound assessment service independently traced wound area and granulation tissue. The quantitative performance of AI tracings was evaluated by statistically comparing error measure distributions between test AI traces and reference human traces (AI vs human) with error distributions between independent traces by 2 humans (human vs human). Quantitative outcomes included statistically significant differences in error measures of false-negative area (FNA), false-positive area (FPA), and absolute relative error (ARE) between AI vs human and human vs human comparisons of wound area and granulation tissue tracings. Six masked attending physician reviewers (3 per site) viewed randomized area tracings for AI and human annotators and qualitatively assessed them. Qualitative outcomes included statistically significant difference in the absolute difference between AI-based PGT measurements and mean reviewer visual PGT estimates compared with PGT estimate variability measures (ie, range, standard deviation) across reviewers. A total of 199 photographs were selected for the study across both sites; mean (SD) patient age was 64 (18) years (range, 17-95 years) and 127 (63.8%) were women. The comparisons of AI vs human with human vs human for FPA and ARE were not statistically significant. AI vs human FNA was slightly elevated compared with human vs human FNA (median [IQR], 7.7% [2.7%-21.2%] vs 5.7% [1.6%-14.9%]; P < .001), indicating that AI traces tended to slightly underestimate the human reference wound boundaries compared with human test traces. Two of 6 reviewers had a statistically higher frequency in agreement that human tracings met the standard area definition, but overall agreement was moderate (352 yes responses of 583 total responses [60.4%] for AI and 793 yes responses of 1166 total responses [68.0%] for human tracings). AI PGT measurements fell in the typical range of variation in interreviewer visual PGT estimates; however, visual PGT estimates varied considerably (mean range, 34.8%; mean SD, 19.6%). This study provides a framework for evaluating AI-based digital wound assessment tools that can be extended to automated measurements of other wound features or adapted to evaluate other AI-based digital image diagnostic tools. As AI-based wound assessment tools become more common across wound care settings, it will be important to rigorously validate their performance in helping clinicians obtain accurate wound assessments to guide clinical care.
Highlights
Chronic wounds cause significant morbidity and mortality and cost the US health care system approximately $25 billion annually.[1]
artificial intelligence (AI) vs human false-negative area (FNA) was slightly elevated compared with human vs human FNA, indicating that AI traces tended to slightly underestimate the human reference wound boundaries compared with human test traces
This study provides a framework for evaluating AI-based digital wound assessment tools that can be extended to automated measurements of other wound features or adapted to evaluate other AI-based digital image diagnostic tools
Summary
Chronic wounds cause significant morbidity and mortality and cost the US health care system approximately $25 billion annually.[1]. While numerous methods can be used to quantify wound area, many clinics still use manual ruler-based measurements, which are subject to high variability and can overestimate the true surface area by as much as 40%.4-7 Another common method of wound measurement is contact acetate tracing, but the contact on a patient’s wound can alter the contour of the border, introduce a source of contamination to patients with increased risk of infection, and induce pain.[8,9] Manual digital planimetry of wound photographs improves accuracy but is still subject to interclinician variability and can be too time consuming to integrate into a high-volume wound care center.[7,10,11] While numerous methods can be used to quantify wound area, many clinics still use manual ruler-based measurements, which are subject to high variability and can overestimate the true surface area by as much as 40%.4-7 Another common method of wound measurement is contact acetate tracing, but the contact on a patient’s wound can alter the contour of the border, introduce a source of contamination to patients with increased risk of infection, and induce pain.[8,9] Manual digital planimetry of wound photographs improves accuracy but is still subject to interclinician variability and can be too time consuming to integrate into a high-volume wound care center.[7,10,11]
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