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Updated: Jun 12, 2026

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Deep learning Algorithm for Wound assessment after total kNee (DAWN) arthroplasty : a prospective study protocol.

Sai Pendyala1, Aditya Vijay1, Nimra Akram1

  • 1Research Department of South West London Elective Orthopaedic Centre (SWLEOC), Surrey, UK.

Bone & Joint Open
|May 19, 2026
PubMed
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This summary is machine-generated.

This study develops an AI tool using deep learning to classify total knee arthroplasty (TKA) wound photos, aiming for early detection of complications and improved patient care.

Area of Science:

  • Orthopaedic surgery
  • Artificial intelligence in medicine
  • Medical imaging analysis

Background:

  • Post-total knee arthroplasty (TKA) wound complications require timely identification.
  • Current wound assessment methods can be subjective and resource-intensive.
  • There is a need for objective, efficient tools to triage orthopaedic surgical wounds.

Purpose of the Study:

  • To develop and internally validate a deep learning (DL) algorithm for classifying TKA wound photographs.
  • To differentiate between wounds 'healing well' and those 'requiring review' using AI.
  • To create a prospectively validated tool for orthopaedic wound triage.

Main Methods:

  • Prospective cohort study at an Elective Orthopaedic Centre.
  • Recruitment of adult patients with primary TKAs experiencing wound concerns.

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  • Collection of standardized wound photographs and symptom survey data within two weeks postoperatively.
  • Independent blinded assessment of wounds by consultant orthopaedic surgeons.
  • Fine-tuning a pre-trained DL algorithm on 80% of the data with five-fold cross-validation.
  • Internal validation using a held-out 20% test set, stratified by outcome and demographics.

    • Main Results:

    • The study aims to generate a DL tool for classifying TKA wound images.
    • Internal validation will assess the algorithm's efficacy in identifying wounds needing review.
    • The developed algorithm is expected to accurately triage orthopaedic wounds based on photographic and symptom data.

    Conclusions:

    • This study will yield one of the first prospectively validated DL tools for orthopaedic wound triage.
    • The AI approach facilitates early complication detection and timely intervention.
    • The DL tool supports NHS digital-first pathways and enhances postoperative patient care and communication.