Inhibition Zones: A Window into Antibiotic Resistance

Abstract

Antibiotic resistance poses a significant global health threat, necessitating innovative approaches to combat it effectively. This survey paper presents a comprehensive overview of the challenges associated with antibiotic resistance and the critical role of antibiotic susceptibility testing (AST) in guiding treatment decisions. We propose leveraging advancements in image processing and machine learning to develop an automated algorithm for measuring the Zone of Inhibition in AST, addressing the limitations of manual methods. Additionally, we discuss the potential of mobile applications and expert systems to simplify AST, particularly in resource-limited settings. Standardization efforts and future trends in AST are also explored to enhance testing strategies and preserve antibiotic efficacy. Through this research, we aim to contribute to the ongoing efforts to mitigate antibiotic resistance and improve patient care worldwide.