Published on in Vol 26 (2024)

This is a member publication of University of Toronto

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52880, first published .
Machine Learning Approaches for the Image-Based Identification of Surgical Wound Infections: Scoping Review

Machine Learning Approaches for the Image-Based Identification of Surgical Wound Infections: Scoping Review

Machine Learning Approaches for the Image-Based Identification of Surgical Wound Infections: Scoping Review

Journals

  1. Rochon M, Tanner J, Jurkiewicz J, Beckhelling J, Aondoakaa A, Wilson K, Dhoonmoon L, Underwood M, Mason L, Harris R, Cariaga K, Serra R. Wound imaging software and digital platform to assist review of surgical wounds using patient smartphones: The development and evaluation of artificial intelligence (WISDOM AI study). PLOS ONE 2024;19(12):e0315384 View
  2. Rochon M, Sandy-Hodgetts K, Betteridge R, Glasbey J, Kariwo K, McLean K, Niezgoda J, Serena T, Tettelbach W, Smith G, Tanner J, Wilson K, Bond-Smith G, Lathan R, Macefield R, Totty J. Remote digital surgical wound monitoring and surveillance using smartphones. Journal of Wound Care 2025;34(Sup4b):S1 View

Conference Proceedings

  1. Rodríguez Prada J, Cote Flórez Á, Pineda Gómez A, Vargas Cardona H. 2024 3rd International Congress of Biomedical Engineering and Bioengineering (CIIBBI). Identification of Alterations in Surgical Wounds Through the Application of Artificial Intelligence in Digital Images View