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 in RLS should be simple, affordable and maintenance-friendly, in contrast with the expensive machinery used in high-income countries for identification, such as such as Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry (MALDI-TOF MS). 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 the approach by acquiring a dataset of clinical isolates: 257 isolates from five species among the most frequent pathogens were grown from samples of various types. Resulting optical morphotypes showed variability, including within the same species: a configuration much closer to field clinical settings than focusing only on reference strains. Despite this variability, high identification performance was achieved: the correct identification rate at the species level was 91.7%. These results open up some new prospects for identification in RLS.