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Related Concept Videos

Phases of Wound Repair01:28

Phases of Wound Repair

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Following injury, the integrity of the injured tissues must be reestablished. For example, in skin tissue, wound repair involves coordination among resident skin cells, blood mononuclear cells, extracellular matrix, growth factors, and cytokines to complete the healing cascade.
Formation of Blood Clot
In case of deep injuries, trauma to blood vessels results in blood loss. In the meantime, phospholipids released from the ruptured endothelial cellular membrane are converted into arachidonic...
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Related Experiment Video

Updated: Aug 3, 2025

Assessment of Acute Wound Healing using the Dorsal Subcutaneous Polyvinyl Alcohol Sponge Implantation and Excisional Tail Skin Wound Models.
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Assessment of Acute Wound Healing using the Dorsal Subcutaneous Polyvinyl Alcohol Sponge Implantation and Excisional Tail Skin Wound Models.

Published on: March 25, 2020

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Towards an AI-Based Objective Prognostic Model for Quantifying Wound Healing.

Rishabh Gupta, Lucas Goldstone, Shira Eisen

    IEEE Journal of Biomedical and Health Informatics
    |April 7, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Deep learning models using wound image features improve chronic wound healing prognosis. Objective and combined models significantly outperformed traditional assessment tools like PUSH and BWAT, showing broad clinical applicability.

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    Area of Science:

    • Wound healing research
    • Medical image analysis
    • Artificial intelligence in healthcare

    Background:

    • Chronic wounds impact millions globally, necessitating accurate prognosis for effective care.
    • Current wound assessment tools (PUSH, BWAT) are subjective, slow, and prone to variability.
    • Objective wound assessment is needed to improve clinical decision-making and treatment efficacy.

    Purpose of the Study:

    • To explore the use of deep learning-based objective features from wound images for prognosis.
    • To develop and validate prognostic models for delayed wound healing.
    • To compare the performance of AI-driven models against standard clinical assessment tools.

    Main Methods:

    • Utilized a large dataset of 2.1 million wound evaluations from over 200,000 wounds.
    • Extracted objective features (wound area, tissue type) from wound images using deep learning.
    • Trained prognostic models using objective features alone and in combination with subjective data.

    Main Results:

    • The objective model showed a 5-9% improvement over PUSH and BWAT.
    • The best performing model (objective + subjective features) improved prognosis by 8-13% over PUSH and BWAT.
    • Models demonstrated consistent outperformance across diverse clinical settings and patient demographics.

    Conclusions:

    • Deep learning models using objective, image-derived features offer a more accurate and generalizable approach to wound healing prognosis.
    • AI-powered tools can enhance the objectivity and efficiency of wound assessment.
    • These findings support the integration of AI in wound care for improved patient outcomes.