Is my wound infected? A study on the use of hyperspectral imaging to assess wound infection

Frontiers in Medicine

Medicine

Gennadi Saiko¹  Jose L. Ramirez-GarciaLuna²  Gregory K. Berry²  Mario A. Martinez-Jimenez³  Robert Bartlett⁴  Zheng Liu⁴  Robert D. J. Fraser⁵  Amy Lorincz⁶ 

[1] Department of Physics, Toronto Metropolitan University, Toronto, ON, Canada;Department of Surgery, McGill University Health Centre, Montreal, QC, Canada;Division of Surgery, Hospital Central “Dr. Ignacio Morones Prieto”, San Luis Potosí, Mexico;Swift Medical, Toronto, ON, Canada;Swift Medical, Toronto, ON, Canada;Arthur Labatt School of Nursing, Northwestern University, London, ON, Canada;Vope Medical, Montreal, QC, Canada;

关键词: wounds;    inflammation;    infection;    fluorescence;    thermography;    hyperspectral imaging;    point-of-care;    bacteria;   

DOI  :  10.3389/fmed.2023.1165281

 received in 2023-05-01, accepted in 2023-07-13,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionClinical signs and symptoms (CSS) of infection are a standard part of wound care, yet they can have low specificity and sensitivity, which can further vary due to clinician knowledge, experience, and education. Wound photography is becoming more widely adopted to support wound care. Thermography has been studied in the medical literature to assess signs of perfusion and inflammation for decades. Bacterial fluorescence has recently emerged as a valuable tool to detect a high bacterial load within wounds. Combining these modalities offers a potential objective screening tool for wound infection.MethodsA multi-center prospective study of 66 outpatient wound care patients used hyperspectral imaging to collect visible light, thermography, and bacterial fluorescence images. Wounds were assessed and screened using the International Wound Infection Institute (IWII) checklist for CSS of infection. Principal component analysis was performed on the images to identify wounds presenting as infected, inflamed, or non-infected.ResultsThe model could accurately predict all three wound classes (infected, inflamed, and non-infected) with an accuracy of 74%. They performed best on infected wounds (100% sensitivity and 91% specificity) compared to non-inflamed (sensitivity 94%, specificity 70%) and inflamed wounds (85% sensitivity, 77% specificity).DiscussionCombining multiple imaging modalities enables the application of models to improve wound assessment. Infection detection by CSS is vulnerable to subjective interpretation and variability based on clinicians' education and skills. Enabling clinicians to use point-of-care hyperspectral imaging may allow earlier infection detection and intervention, possibly preventing delays in wound healing and minimizing adverse events.

【 授权许可】

Unknown   
Copyright © 2023 Ramirez-GarciaLuna, Martinez-Jimenez, Fraser, Bartlett, Lorincz, Liu, Saiko and Berry.