I-Corps: Portable System for Pathogenic Bacteria Characterization

Award ID: 2332586

The broader impact/commercial potential of this I-Corps project is the development of a portable diagnostic system to identify pathogenic bacteria. Currently, most diagnostic detection methods include growing cultures in vitro coupled with antigen-based or polymerase chain reaction (PCR)-based detection assays. However, these methods are often slow and ineffective, especially for respiratory bacterial infections. The proposed technology is based on based on optical scatter data combined with machine learning models and optimization. It is a portable, automated approach that is designed to achieve rapid detection of respiratory pathogens/bacteria with reduced classification times. The proposed technology may have a broad impact on the health care industry and patient outcomes, particularly respiratory infections in children and the elderly. In addition, this technology has the potential to accelerate advances in clinical diagnosis of pathogenic bacteria. This I-Corps project is based on the development of a portable diagnostic system for pathogenic bacteria detection and identification. The proposed technology combines optical scatter data with machine learning models and optimization to enable the evaluation and characterization of bacteria patterns and signatures. The system is designed using light scattering of particles in the pathogenic bacteria with the goal of providing a rapid, real-time, portable system for detection and characterization of pathogenic bacteria. Preliminary results with four different bacteria showed that optical signatures were obtained and that different bacteria can be distinguished. The proposed system may be valuable to clinicians and researchers for rapidly detecting the patterns of specific bacterial infections. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Ruth Shuman