Related Experiment Video
Updated: Sep 23, 2025
Evaluating Regional Pulmonary Deposition using Patient-Specific 3D Printed Lung Models
Published on: November 11, 2020
Quantifying Simulated Contamination Deposition on Healthcare Providers Using Image Analysis
Yiqun Lin1, Adam Cheng, Jonathan Pirie
1From the KidSIM Simulation Program (Y.L., J.D.), Alberta Children's Hospital; Departments of Pediatrics and Emergency Medicine (A.C.), University of Calgary, Calgary; Pediatric Emergency Medicine Simulation Program (J.P.), The Hospital for Sick Children University of Toronto, Toronto; Departments of Paediatric Emergency Medicine and Paediatrics (A.L., M.B.), University of Montréal Sainte-Justine's Hospital University Centre, Montréal; Department of Anesthesia and Pain Medicine (C.M.), The Hospital for Sick Children, Toronto; Department of Mechanical Engineering (C.-E.A., E.R.), Polytechnique Montréal, Montréal; Department of Veterinary Clinical and Diagnostic Sciences (K.H.), Faculty of Veterinary Medicine University of Calgary, Calgary; Department of Family Medicine and Emergency Medicine (G.G.), Laval University Laval University Hospital Center, Québec City, Canada; and Children's Hospital Los Angeles (T.P.C.), University of Southern California, Los Angeles, CA.
Introduction:
Simulation-based research has played an important role in improving care for communicable diseases. Unfortunately, few studies have attempted to quantify the level of contamination in these simulation activities. We aim to assess the feasibility and provide validity evidence for using integrated density values and area of contamination (AOC) to differentiate various levels of simulated contamination.
Methods:
An increasing number of simulated contamination spots using fluorescent marker were applied on a manikin chest to simulate a contaminated healthcare provider. An ultraviolet light was used to illuminate the manikin to highlight the simulated contamination. Images of increasing contamination levels were captured using a camera with different exposure settings. Image processing software was used to measure 2 outcomes: (1) natural logarithm of integrated density; and (2) AOC. Mixed-effects linear regression models were used to assess the effect of contamination levels and exposure settings on both outcome measures. A standardized "proof-of-concept" exercise was set up to calibrate and formalize the process for human subjects.
Results:
A total of 140 images were included in the analyses. Dose-response relationships were observed between contamination levels and both outcome measures. For each increment in the number of contaminated simulation spots (ie, simulated contaminated area increased by 38.5 mm 2 ), on average, log-integrated density increased by 0.009 (95% confidence interval, 0.006-0.012; P < 0.001) and measured AOC increased by 37.8 mm 2 (95% confidence interval, 36.7-38.8 mm 2 ; P < 0.001), which is very close to actual value (38.5 mm 2 ). The "proof-of-concept" demonstration further verified results.
Conclusions:
Integrated density and AOC measured by image processing can differentiate various levels of simulated, fluorescent contamination. The AOC measured highly agrees with the actual value. This method should be optimized and used in the future research to detect simulated contamination deposited on healthcare providers.
More Related Videos
06:36Effects of Mechanical Methods Used in Peri-implantitis Treatment on Implant Surface Decontamination and Roughness
Published on: March 14, 2025
06:20Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition
Published on: March 11, 2021