Exploiting Image Processing and Artificial Intelligence Techniques for the Determination of Antimicrobial Susceptibility
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Mdpi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Antimicrobial susceptibility tests, achieved through the use of antibiotic-impregnated disks in a suitable laboratory environment, are conducted to determine which antibiotics are effective against the bacteria present in the body of an infected patient. The Kirby-Bauer method, a type of disk diffusion antimicrobial susceptibility test, is currently widely applied in microbiology laboratories due to its proven effectiveness. In our study, we developed an algorithm that utilizes image processing techniques to detect the inhibition zones of bacteria. A certain color depth acts as the threshold for the inhibition zone, with its radius determined according to the size of the reference object. This approach facilitates the measurement of inhibition zones and employs machine learning and deep learning to categorize antibiograms, followed by determination of whether a bacterium on the disk is sensitive or resistant to the antibiotics applied. The focus of this research is creating an automated interpretation system for antimicrobial susceptibility testing using the disk diffusion technique, thus simplifying the measurement and interpretation of inhibition zone sizes.
Açıklama
Anahtar Kelimeler
Antimicrobial Susceptibility Test, Disk Diffusion Test, Artificial Intelligence, Image Processing, Machine Learning, Deep Learning, Transfer Learning
Kaynak
Applied Sciences-Basel
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
14
Sayı
9
