Application of a Bacterial Image Analysis System for Antibiotic Susceptibility Test Detection

In 2020, due to the impact of the COVID-19 pandemic, the misuse of antibiotics has become more severe. This not only results in ineffective treatments but also contributes to increased bacterial antibiotic resistance, leading to the emergence of superbugs. Therefore, this we focuses on developing a bacterial image analysis system applicable to Antimicrobial Susceptibility Testing. The system employs phase-contrast microscopy to monitor bacterial cultivation within a micro fluidic device. The research entails the creation of a tailor-made micro fluidic chip integrated with an imaging system, facilitating the individual observation of bacteria. The system offers the advantage of minimizing liquid usage. The primary focus of this research is on calculating bacterial quantities and determining antibiotic resistance based on surface morphology. Convolutional Neural Networks, including YOLOv7, YOLOv8, U-Net, U-Net++, and U-Net3+, are employed for bacterial counting and surface morphology recognition. Ultimately, the performance validation results indicate that U-Net3+ and YOLOv7 exhibit prominent performance in model verification, demonstrating effective bacterial recognition. Subsequently, this system aims to enhance the detection of bacterial antibiotic resistance, contributing substantively to single-bacteria/cell recognition in the field of biological applications.