Predicting and identifying antimicrobial resistance in the marine environment using AI and machine learning.

Fough, Faranak; Zhao, Yafan; Shah, Faisal Mehmood

Abstract

Antimicrobial resistance (AMR) poses a serious threat to public health and serves as a vital reservoir for resistant microorganisms. Antimicrobial resistance (AMR) is an increasingly critical public health issue that requires 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. This study focuses on evaluating the diameters of the disc diffusion zone and employs Artificial Intelligence (AI) and Machine Learning (ML) techniques such as image segmentation, data augmentation, and deep learning methods to enhance accuracy in predicting microbial resistance.

Citation

FOUGH, F., ZHAO, Y. and SHAH, F.M. 2024. Predicting and identifying antimicrobial resistance in the marine environment using AI and machine learning. In Proceedings of the 31st IEEE (Institute of Electrical and Electronics Engineers) International conference on electronics circuits and systems 2024 (IEEE ICECS2024), 18-20 November 2024, Nancy, France. Piscataway: IEEE [online], article 10849269. Available from: https://doi.org/10.1109/ICECS61496.2024.10849269

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 31st IEEE (Institute of Electrical and Electronics Engineers) International conference on electronics circuits and systems 2024 (IEEE ICECS2024)
• Start Date: Nov 18, 2024
• End Date: Nov 20, 2024
• Acceptance Date: Jun 17, 2024
• Online Publication Date: Nov 18, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Jan 31, 2025
• Publicly Available Date: Jan 31, 2025
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Article Number: 10849269
• Series ISSN: 2995-0589
• DOI: https://doi.org/10.1109/ICECS61496.2024.10849269
• Keywords: Artificial intelligence; Machine learning methods; Inhibition zone measurement; Convolutional neural networks; Antimicrobial susceptibility test
• Public URL: https://rgu-repository.worktribe.com/output/2675546