Highly sensitive quantitative phase microscopy and deep learning complement whole genome sequencing for rapid detection of infection and antimicrobial resistance

The current state-of-the-art infection and antimicrobial resistance diagnostics (AMR) is based mainly on culture-based methods with detection time of 48-96 hours. Slow diagnoses lead to adverse patient outcomes that directly correlate with the time taken to administer optimal antimicrobial. Mortality risk doubles with a 24-hour delay in providing appropriate antibiotics in cases of bacteremia. It is therefore essential to develop novel methods that can promptly and accurately diagnose microbial infections both at species and strain levels in clinical settings. Here, we demonstrate that the complimentary use of label-free optical assay with whole-genome sequencing (WGS) can enable high-speed culture-free diagnosis of AMR. Our assay is based on microscopy methods exploiting label free highly sensitive quantitative phase microscopy (QPM) followed by deep convolutional neural networks (DCNNs) based classification. We benchmarked our proposed workflow on twenty-one clinical isolates from four WHO priority pathogens that were antibiotic susceptibility testing (AST) phenotyped and their antimicrobial resistance (AMR) profile was determined by WGS. The proposed optical assay is in good agreement with the WGS characterization. Highly accurate classification based on the gram staining (100% for gramnegative and 83.4% for gram-positive), species (98.64), and wild-type/non-wild type (96.45%), as well as at the individual strain level (100% accurate in predicting 18 out of the 21 strains). These results demonstrate the potential of the QPM assay as a rapid and first-stage tool for species, resistance, and strain-level classification, which WGS can follow up for confirmation. Taken together, all this information is of high clinical importance. Such a workflow can facilitate efficient antimicrobial stewardship and prevent the spread of AMR.