Antimicrobial resistance (AMR) is an increasingly critical public health issue necessitating precise and efficient methodologies to achieve prompt results. The accurate and early detection of AMR is crucial, as its absence can pose life-threatening risks to diverse ecosystems, including the marine environment. The spread of AMR among microorganisms in the marine environment can have significant consequences, potentially impacting human life directly. This study focuses on evaluating the diameters of the disc diffusion zone and employs artificial intelligence and machine learning techniques such as image segmentation, data augmentation, and deep learning methods to enhance accuracy and predict microbial resistance.
Citation
FOUGH, F., JANJUA, G., ZHAO, Y. and DON, A.W. 2023. Predicting and identifying antimicrobial resistance in the marine environment using AI and machine learning algorithms. In Proceedings of the 2023 IEEE (Institute of Electrical and Electronics Engineers) International workshop on Metrology for the sea (MetroSea 2023); learning to measure sea health parameters, 4-6 October 2023, La Valletta, Malta. Piscataway: IEEE [online], pages 121-126. Available from: https://doi.org/10.1109/MetroSea58055.2023.10317294
Presentation Conference Type
Conference Paper (published)
Conference Name
2023 IEEE (Institute of Electrical and Electronics Engineers) International workshop on Metrology for the sea (MetroSea 2023); learning to measure sea health parameters
Start Date
Oct 4, 2023
End Date
Oct 6, 2023
Acceptance Date
Jun 30, 2023
Online Publication Date
Dec 31, 2023
Publication Date
Dec 31, 2023
Deposit Date
Jan 11, 2024
Publicly Available Date
Jan 11, 2024
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
