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

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.