Predicting and Identifying Antimicrobial Resistance in the Marine Environment Using AI and Machine Learning

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.