Towards automated detection, semi-quantification and identification of microbial growth in clinical bacteriology: A proof of concept

Background: Automation in microbiology laboratories impacts management, workflow, productivity and quality. Further improvements will be driven by the development of intelligent image analysis allowing automated detection of microbial growth, release of sterile samples, identification and quantification of bacterial colonies and reading of AST disk diffusion assays. We investigated the potential benefit of intelligent imaging analysis by developing algorithms allowing automated detection, semi-quantification and identification of bacterial colonies. Methods: Defined monomicrobial and clinical urine samples were inoculated by the BD Kiestra™ InoqulA™ BT module. Image acquisition of plates was performed with the BD Kiestra™ ImagA BT digital imaging module using the BD Kiestra™ Optis™ imaging software. The algorithms were developed and trained using defined data sets and their performance evaluated on both defined and clinical samples. Results: The detection algorithms exhibited 97.1% sensitivity and 93.6% specificity for microbial growth detection. Moreover, quantification accuracy of 80.2% and of 98.6% when accepting a 1 log tolerance was obtained with both defined monomicrobial and clinical urine samples, despite the presence of multiple species in the clinical samples. Automated identification accuracy of microbial colonies growing on chromogenic agar from defined isolates or clinical urine samples ranged from 98.3% to 99.7%, depending on the bacterial species tested. Conclusion: The development of intelligent algorithm represents a major innovation that has the potential to significantly increase laboratory quality and productivity while reducing turn-around-times. Further development and validation with larger numbers of defined and clinical samples should be performed before transferring intelligent imaging analysis into diagnostic laboratories.