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  3. Toward Objective Wound Edge Classification In Clinical Practice.
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  3. Toward Objective Wound Edge Classification In Clinical Practice.

Related Experiment Video

Murine Excisional Wound Healing Model and Histological Morphometric Wound Analysis
06:36

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Published on: August 21, 2020

Toward Objective Wound Edge Classification in Clinical Practice.

Corrado Zengarini1,2, Tommaso Giacometti3,4, Yuri Merli1,2

  • 1Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.

Experimental Dermatology
|June 6, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Automated wound edge analysis using image data can improve consistency in chronic wound evaluation. Combining geometric and visual features in AI models achieved expert-level agreement, reducing subjectivity in wound assessment.

Keywords:
computer visionmachine learningsurface meshingwoundwound healing

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Area of Science:

  • Medical imaging
  • Computational pathology
  • Wound care technology

Background:

  • Chronic wound evaluation relies on subjective wound edge assessment.
  • Inter-observer variability is a significant issue, especially with 2D clinical photographs.
  • Objective and reproducible methods are needed to enhance wound assessment accuracy.

Purpose of the Study:

  • To assess the subjectivity of clinical wound edge assessment.
  • To develop and evaluate an automated image-analysis pipeline for wound edge characterization.
  • To determine if geometric and visual features can improve agreement in wound edge classification.

Main Methods:

  • Retrospective analysis of 1,860 wound images from routine clinical practice.
  • Independent annotation of images by four expert clinicians.
  • Development of an automated pipeline for wound segmentation, border standardization, and 3D edge profile estimation.
  • Testing geometry-derived profiles and incorporating visual descriptors (shape, color, pattern) into a supervised classifier.

    • Main Results:

    • Low inter-clinician agreement confirmed the subjectivity of wound edge classification.
    • Geometry-based analysis alone showed poor correspondence with clinical annotations.
    • A supervised classifier using both geometric and visual features achieved agreement comparable to or exceeding inter-clinician agreement.

    Conclusions:

    • Clinical wound edge assessment is influenced by visual cues beyond edge geometry.
    • Automated image-based analysis, incorporating diverse features, shows potential for more reproducible wound edge assessment.
    • External validation in varied clinical settings is crucial for automated systems.