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

ABSTRACT:

Background

Surgical site infections (SSIs) occur frequently and impact patients and health care systems. Remote surveillance of surgical wounds is currently limited by the need for manual assessment by clinicians. Machine learning (ML)-based methods have recently been used to address various aspects of the postoperative wound healing process and may be used to improve the scalability and cost-effectiveness of remote surgical wound assessment.

Objective

The objective of this review was to provide an overview of the ML methods that have been used to identify surgical wound infections from images.

Methods

We conducted a scoping review of ML approaches for visual detection of SSIs following the JBI (Joanna Briggs Institute) methodology. Reports of participants in any post

SUBMITTER: Tabja Bortesi JP 

PROVIDER: S-EPMC10835585 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature