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The escalating threat of antimicrobial resistance (AMR) underscores the critical role of accurate AMR tests in healthcare. Disk-diffusion tests, a cornerstone in determining bacterial susceptibility require accurate inhibition zone measurement. Automatic inhibition zone measurement using computer vision offers significant advantages for assessing bacterial susceptibility in disk-diffusion tests. This method enhances accuracy, as manual measurements can vary between technicians. Automation ensures precise, consistent results by employing image analysis to gauge inhibition zones. It’s a time-saver, enabling rapid processing of large sample volumes — crucial for busy labs. The integration of these systems with existing databases means that data is captured and stored systematically, leading to efficient record-keeping. By standardizing the measurement process, results from different tests and labs can be reliably compared, aiding in the robust analysis of bacterial resistance patterns. Moreover, with the reduction of hands-on handling, the risk of exposure to infectious agents decreases, promoting a safer work environment. The proposed algorithm showcases enhanced sensitivity, highlighting subtle differences that might go unnoticed by the human eye, thereby ensuring more accurate interpretations. A comparative analysis with existing programs will highlight the efficacy of the new algorithm, emphasizing its advantages in precision and reliability. Proposed algorithm addresses challenges low contrast and indistinct zone boundaries through sophisticated image pre-processing. This advanced approach allows for accurate measurement of non-circular or overlapping zones — a task that can prove difficult for manual methods. This advancement in microbial testing technology contributes to more effective patient treatment, addressing the growing importance of bacteriological analysis in healthcare.
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