Simple imaging system for label-free identification of bacterial pathogens in resource-limited settings

Fast, accurate and affordable bacterial identification methods are paramount for timely treatment of infections, especially in resource-limited settings (RLS). However, today only 1.3% of the sub-Saharan African diagnostic laboratories are performing clinical bacteriology. To improve this, diagnostic tools for RLS should prioritize simplicity, affordability, and ease of maintenance, as opposed to the costly equipment utilized for bacterial identification in high-income countries, such as such as Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry (MALDI-TOF MS). In this work, we present a new high-throughput approach based on a simple wide field (864 mm2) lensless imaging system allowing for the acquisition of a large portion of a Petri dish coupled with a supervised deep learning algorithm for identification at the bacterial colony scale. This wide-field imaging system is particularly well suited to RLS since it includes neither moving mechanical parts nor optics. We validated this approach by the acquisition and the subsequent analysis comprising 257 clinical isolates from five species, encompassing some of the most prevalent pathogens. The resulting optical morphotypes exhibited intra- and inter- species variability, a scenario more akin to the real-world clinical practice rather than the one achievable solely concentrating on reference strains. Despite the variability, high identification performance was achieved with a correct species identification rate of 91.7 %. These results open up some new prospects for identification in RLS.