Assessing Clinician Consistency in Wound Tissue Classification and the Value of AI-Assisted Quantification: A Cross-Sectional Study

Abstract

This study investigated the relationship between clinician assessments and the AI-generated scores, highlighting how correlations vary based on clinician expertise. It also explored the proportion of tissue types identified by clinicians relative to AI assessments and assess the inter-clinician agreement in quantifying tissue types, identifying variations based on clinician experience. A cross-sectional survey used purposive, non-random sampling to recruit 50 wound care clinicians. Participants reported their specialisation and experience level before identifying and quantifying granulation, slough, eschar, and epithelialisation in nine wound images. An AI model analysed the same images for comparison. Experienced clinicians and wound care specialists reported higher confidence in assessments. Inter-clinician agreement was moderate-good for granulation and slough (ICC: 0.763-0.762) and moderate-excellent for eschar (ICC: 0.910), but moderate-poor for epithelialisation (ICC: 0.435). Clinicians strongly correlated with AI for granulation, slough, and eschar (r = 0.879, 0.955 and 0.984, respectively). Epithelialisation was more challenging, with a 60% identification rate and moderate correlation with AI (r = 0.579). AI-generated scores aligned with clinician assessments for granulation, slough, and eschar. However, epithelialisation, which is crucial for objectively measuring healing progress, showed greater variability, suggesting that AI could improve the reliability of its assessment, potentially leading to more consistent wound evaluation to guide treatment decisions.

Keywords: AI‐driven wound care; epithelialisation assessment; granulation tissue quantification; inter‐clinician agreement; slough and eschar identification.

FIGURE 1

AI‐wound captured images included in the survey.

FIGURE 2

Percentage of clinicians matching AI‐identified presence or absence of tissue types across nine wound images.

FIGURE 3

(a) Inter‐clinicians' agreement in quantifying tissue types by experience and practice pattern. (b) Mean tissue percentages identified by clinicians, stratified by experience and practice pattern.

FIGURE 4

(a) AI vs. clinicians' scores and correlation between clinician and AI measurements for all participants for each tissue type across wounds. (b) AI vs. specialists' scores and correlation between specialists and AI measurements for each tissue type across wounds. (c) AI vs. experienced clinicians' scores and correlation between scores of clinicians with > 10 years' experience and AI measurements for each tissue type across wounds.