This paper is in the following e-collection/theme issue:

Digital Health Reviews (1471) Clinical Information and Decision Making (1600) Machine Learning (1680) Clinical Informatics (1127) Imaging Informatics (223) Medical imaging (52)

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

Authors of this article:

Juan Pablo Tabja Bortesi1 Author Orcid Image ;   Jonathan Ranisau1 Author Orcid Image ;   Shuang Di1, 2 Author Orcid Image ;   Michael McGillion3 Author Orcid Image ;   Laura Rosella2 Author Orcid Image ;   Alistair Johnson4 Author Orcid Image ;   PJ Devereaux3 Author Orcid Image ;   Jeremy Petch1, 3, 5, 6 Author Orcid Image
Article Authors Cited by (3) Tweetations (3) Metrics

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