JMIR Publications

ABSTRACT

Background:

Surgical site infections (SSIs) occur frequently and impact patients and healthcare systems. Re-mote 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 Jo-anna Briggs Institute (JBI) methodology. Reports of participants in any postoperative context focusing on identification of surgical wound infections were included. Studies that did not ad-dress SSI identification, surgical wounds, or did not use image or video data were excluded. We searched MEDLINE, Embase, CINAHL, CENTRAL, Web of Science Core Collection, IEEE Xplore, Compendex, and arXiv for relevant studies in November 2022. The records retrieved were double screened for eligibility. A data extraction tool was used to chart the relevant data, which was described narratively and presented using charts and tables. Employment of Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines was evaluated and the Prediction model Risk of Bias Assessment Tool (PROBAST) was used to assess risk of bias.

Results:

Ten of the 715 unique records screened met the eligibility criteria. In these studies, the clinical contexts and surgical procedures were diverse. All papers developed diagnostic models, though none performed external validation. Both traditional ML and deep learning methods were used to identify SSIs from mostly colour images, and the volume of images used ranged from under fifty to thousands. Ten TRIPOD items had at least 40% use across the studies, though fifteen items had been reported below 40%. PROBAST assessment led to nine studies being identified as having an overall high risk of bias, with one study having overall unclear risk of bias.

Conclusions:

Research on the image-based identification of surgical wound infections using ML remains novel, and there is a need for standardized reporting. Limitations related to variability in image capture, model building, and data sources should be addressed in the future.