Wound Size Imaging: Ready for Smart Assessment and Monitoring

Yves Lucas, Rania Niri, Sylvie Treuillet, Hassan Douzi, Benjamin Castaneda

Research output: Contribution to journalReview articlepeer-review

24 Scopus citations

Abstract

Significance: We introduce and evaluate emerging devices and modalities for wound size imaging and also promising image processing tools for smart wound assessment and monitoring. Recent Advances: Some commercial devices are available for optical wound assessment but with limited possibilities compared to the power of multimodal imaging. With new low-cost devices and machine learning, wound assessment has become more robust and accurate. Wound size imaging not only provides area and volume but also the proportion of each tissue on the wound bed. Near-infrared and thermal spectral bands also enhance the classical visual assessment. Critical Issues: The ability to embed advanced imaging technology in portable devices such as smartphones and tablets with tissue analysis software tools will significantly improve wound care. As wound care and measurement are performed by nurses, the equipment needs to remain user-friendly, enable quick measurements, provide advanced monitoring, and be connected to the patient data management system. Future Directions: Combining several image modalities and machine learning, optical wound assessment will be smart enough to enable real wound monitoring, to provide clinicians with relevant indications to adapt the treatments and to improve healing rates and speed. Sharing the wound care histories of a number of patients on databases and through telemedicine practice could induce a better knowledge of the healing process and thus a better efficiency when the recorded clinical experience has been converted into knowledge through deep learning.

Original languageEnglish
Pages (from-to)641-661
Number of pages21
JournalAdvances in Wound Care
Volume10
Issue number11
DOIs
StatePublished - 1 Nov 2021
Externally publishedYes

Keywords

  • computer vision
  • deep learning
  • mobile health
  • tissue classification
  • wound size imaging

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