[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123743-en":3,"doc-seo-123743-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123743,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting and Identifying Antimicrobial Resistance in the Marine Environment Using AI and Machine Learning Algorithms","Antimicrobial resistance (AMR) is an increasingly critical public health issue requiring precise, efficient methods to produce prompt results. Accurate and early detection is essential because failure can create life-threatening risks for ecosystems, including the marine environment. AMR can spread among marine microorganisms, with potential direct consequences for human health. This study evaluates disc diffusion zone diameters using AI/ML, combining image segmentation, data augmentation, and deep learning to improve accuracy and predict microbial resistance.","Predicting and identifying antimicrobial resistance in the marine environment using AI and machine learning algorithms .  \nFOUGH, F., JANJUA, G., ZHAO, Y., DON, A.W.  \n2023  \n© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.  \nPredicting and Identifying Antimicrobial Resistance in the Marine Environment Using AI & Machine  \nLearning Algorithms  \nFaranak Fough Dr Ghalib Janjua Dr Yafan Zhao Dr Aakash Welgamage Don  \nSchool of Engineering School of Engineering School of Engineering School of Pharmacy and Life Sciences Robert Gordon University Robert Gordon University Robert Gordon University Robert Gordon University  \nAberdeen, United Kingdom Aberdeen, United Kingdom Aberdeen, United Kingdom Aberdeen, United Kingdom [f.fough@rgu.ac.uk](f.fough@rgu.ac.uk) [g.janjua@rgu.ac.uk](g.janjua@rgu.ac.uk) [y.zhao@rgu.ac.uk](y.zhao@rgu.ac.uk) [a.welgamage-don@rgu.ac.uk](a.welgamage-don@rgu.ac.uk)  \nAbstract—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 lifethreatening 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.  \nIndex Terms—Artificial intelligence, Machine Learning methods, Inhibition zone measurement, Convolutional Neural Networks, Antimicrobial susceptibility test  \nI. INTRODUCTION  \nAntimicrobial resistance (AMR) poses a significant threat to diverse ecosystem components, including human, animal and environmental [1] . In this study, our focus is specifically directed towards the microorganisms inhabiting marine environments. The development and transmission of AMR between microorganisms in marine environments can be attributed to various factors, such as horizontal gene transfer, gene mutation, and intrinsic resistance mechanisms [2] . It’s worth noting that not all marine microorganisms exhibit resistance, and not all possess the capacity to develop resistance mechanisms. In this regard, human activities such as the discharge of treated wastewater, agricultural runoff, aquaculture practices, and the deposition of conventional and nuclear wastes have exacerbated this issue [3] . AMR has been detected in marine environments worldwide, including coastal waters and marine sediments. Numerous studies have raised concerns about the spread of AMR from marine microorganisms to other environments, with significant implications for public health [4] . Accurate analysis and measurement of AMR in marine microorganisms are essential for understanding the potential problems associated with environmental pollution and the spread of resistance. Therefore, the need for rapid and accurate tools to address these issues becomes critical. While various techniques exist for identifying and diagnosing bacterial  \nsusceptibility or resistance to antimicrobial agents in clinical settings, the most commonly used method in this field is the disc diffusion method [5] . Laboratories employ this method due to its simplicity, cost-effectiveness, and established interpretive criteria [5] . However, there are several challenges associated with its use that can impact result transparency, including delayed response time, labour-intensi","cbCaigkNJnd3KC6I","https://ap.wps.com/l/cbCaigkNJnd3KC6I","pdf",1343058,1,7,"English","en",105,"# Abstract\n# Introduction\n## AMR risks in marine ecosystems\n## Disc diffusion method and limitations\n## Role of AI and machine learning\n# Study objective and approach","[{\"question\":\"Why is early AMR detection in marine environments important?\",\"answer\":\"Early detection is crucial because undetected AMR can lead to life-threatening risks for ecosystems, including marine systems, and may affect human health.\"},{\"question\":\"What method does the study focus on for measuring AMR?\",\"answer\":\"The study focuses on disc diffusion zone diameters, using AI and machine learning to calculate inhibition-zone measurements from images.\"},{\"question\":\"Which AI/ML techniques are used to improve prediction accuracy?\",\"answer\":\"The approach uses image segmentation, data augmentation, and deep learning—particularly convolutional neural network methods—for more accurate resistance prediction.\"}]","Predicting and Identifying Antimicrobial Resistance in the Marine Environment Using AI and Machine Learning Algorithms | 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is early AMR detection in marine environments important?","Question",{"text":75,"@type":76},"Early detection is crucial because undetected AMR can lead to life-threatening risks for ecosystems, including marine systems, and may affect human health.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What method does the study focus on for measuring AMR?",{"text":80,"@type":76},"The study focuses on disc diffusion zone diameters, using AI and machine learning to calculate inhibition-zone measurements from images.",{"name":82,"@type":73,"acceptedAnswer":83},"Which AI/ML techniques are used to improve prediction accuracy?",{"text":84,"@type":76},"The approach uses image segmentation, data augmentation, and deep learning—particularly convolutional neural network methods—for more accurate resistance 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