Early Classification of Gram-negative Bacteria with Colony Imaging and Deep Learning without Coding Experience release_krpqvtg5kfhwtevuxu5vnts6uq

Abstract

Bacterial colony morphology is the first step in classifying bacterial species during the microbial identification process. It is very important to assess the morphology of bacterial colonies in a preliminary screening process to largely reduce the scope of possible bacteria species and increase work productivity in clinical bacteriology by making later identification more specific. However, making a decision about this topic requires sufficient clinical laboratory expertise. Teachable Machine® is a rapid, easy-to-use, web-based tool accessible to everyone that is used to create machine learning models. In this study, the performance of Teachable Machine® was assessed for cheap, rapid and practical identification of enteric and non-fermenting bacteria frequently isolated in microbiology laboratories. A total of 1202 colony images were used to train and validate the network's diagnostic performance. Additionally, 80 representative test images were used to assess performance. Level 1 was defined as E. coli-K. pneumonia, Level 2 was defined as P. aeruginosa-A. baumannii, Level 3 was defined as enteric bacteria-non-fermenting bacteria and Level 4 was defined as differentiating these four pathogens from each other. Mean accuracy of Teachable Machine® for the defined classes was 96.7%, 94.1%, 94.3%, and 90.3% for Levels 1, 2, 3, and 4, respectively. General accuracy for classification of the 80 representative colonies was 82.5% and the hit rates were 85.0%, 100%, 75.0%, and 70.0% for E. coli, K. pneumoniae, P. aeruginosa and A. baumannii, respectively. This cost-effective bacterial identification system, supported by deep learning, will be an important pioneer for a variety of applications in clinical microbiology by reducing the identification process by a significant degree and automating identification of colonies without requiring a specialist.
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