Pressure injury image analysis with machine learning techniques: A systematic review on previous and possible future methods

Sofia Zahia, Maria Begoña Garcia Zapirain, Xavier Sevillano, Alejandro González, Paul J. Kim, Adel Elmaghraby

Research output: Contribution to journalReview article

1 Scopus citations

Abstract

Pressure injuries represent a tremendous healthcare challenge in many nations. Elderly and disabled people are the most affected by this fast growing disease. Hence, an accurate diagnosis of pressure injuries is paramount for efficient treatment. The characteristics of these wounds are crucial indicators for the progress of the healing. While invasive methods to retrieve information are not only painful to the patients but may also increase the risk of infections, non-invasive techniques by means of imaging systems provide a better monitoring of the wound healing processes without causing any harm to the patients. These systems should include an accurate segmentation of the wound, the classification of its tissue types, the metrics including the diameter, area and volume, as well as the healing evaluation. Therefore, the aim of this survey is to provide the reader with an overview of imaging techniques for the analysis and monitoring of pressure injuries as an aid to their diagnosis, and proof of the efficiency of Deep Learning to overcome this problem and even outperform the previous methods. In this paper, 114 out of 199 papers retrieved from 8 databases have been analyzed, including also contributions on chronic wounds and skin lesions.

Original languageEnglish (US)
Article number101742
JournalArtificial Intelligence in Medicine
Volume102
DOIs
StatePublished - Jan 2020
Externally publishedYes

Keywords

  • Deep learning
  • Machine learning algorithms
  • Pressure injury
  • Wound image analysis

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence

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