The detection and prediction of surgical site infections using multi-modal sensors and machine learning: Results in an animal model

Charmayne Mary Lee Hughes; Andrew Jeffers; Arun Sethuraman; Michael Klum; Milly Tan; Valerie Tan

doi:10.3389/fmedt.2023.1111859

The detection and prediction of surgical site infections using multi-modal sensors and machine learning: Results in an animal model

Front Med Technol. 2023 Apr 17:5:1111859. doi: 10.3389/fmedt.2023.1111859. eCollection 2023.

Authors

Charmayne Mary Lee Hughes¹, Andrew Jeffers², Arun Sethuraman², Michael Klum², Milly Tan², Valerie Tan²

Affiliations

¹ Health Equity Institute NeuroTech Laboratory, San Francisco State University, San Francisco, CA, United States.
² Crely Healthcare Pte. Limited, Singapore, Singapore.

Abstract

Introduction: Surgical Site Infection (SSI) is a common healthcare-associated infection that imposes a considerable clinical and economic burden on healthcare systems. Advances in wearable sensors and digital technologies have unlocked the potential for the early detection and diagnosis of SSI, which can help reduce this healthcare burden and lower SSI-associated mortality rates.

Methods: In this study, we evaluated the ability of a multi-modal bio-signal system to predict current and developing superficial incisional infection in a porcine model infected with Methicillin Susceptible Staphylococcus Aureus (MSSA) using a bagged, stacked, and balanced ensemble logistic regression machine learning model.

Results: Results demonstrated that the expression levels of individual biomarkers (i.e., peri-wound tissue oxygen saturation, temperature, and bioimpedance) differed between non-infected and infected wounds across the study period, with cross-correlation analysis indicating that a change in bio-signal expression occurred 24 to 31 hours before this change was reflected by clinical wound scoring methods employed by trained veterinarians. Moreover, the multi-modal ensemble model indicated acceptable discriminability to detect the presence of a current superficial incisional SSI (AUC = 0.77), to predict an SSI 24 hours in advance of veterinarian-based SSI diagnosis (AUC = 0.80), and to predict an SSI 48 hours in advance of veterinarian-based SSI diagnosis (AUC = 0.74).

Discussion: In sum, the results of the current study indicate that non-invasive multi-modal sensor and signal analysis systems have the potential to detect and predict superficial incisional SSIs in porcine subjects under experimental conditions.

Keywords: animal model; machine learning; multi-modal sensors; non-invasive sensors; superficial incisional infection; surgical site infection; wound healing.