Accuracy of classification of urinary Gram-stain findings by a computer-aided diagnosis app compared with microbiology specialists

<b>Introduction.</b> Timely and accurate diagnosis of bacterial infections enables early administration of appropriate antimicrobial treatment and improved outcomes.<b>Hypothesis/Gap Statement.</b> The accuracy of computer-aided diagnosis (CAD) for identifying organisms on urine Gram stains has not been compared with that of microbiology specialists (MS).<b>Aim.</b> To compare the interpretation of urine Gram-stain results by MS and a CAD app designed using artificial intelligence.<b>Methodology.</b> Urine specimens from patients with urinary tract infections were used and collected at two tertiary hospitals between 1 April and 31 December 2022. Using non-inferiority analysis to assess whether CAD was non-inferior to expert interpretation, CAD-predicted microscopic findings of the Gram-stained slide generated from iPhone camera images from two hospitals were compared with those from ten MS. A total of 153 images were taken from each hospital, and CAD interpreted a total of 306. The primary endpoint was the prediction accuracy based on the morphology of the Gram-stained bacteria.<b>Results.</b> The accuracy (95% confidence interval) of MS and CAD predictions was 83.0% (81.6%-84.3%) and 87.9% (83.7%-91.3%), respectively, with a difference of -4.93% (-8.43% to -0.62%) indicating non-inferiority of CAD.<b>Conclusion.</b> CAD was non-inferior to MS predictions for identifying Gram-stained pathogens; therefore, CAD was suggested to have the potential for guiding empirical antibiotic selection in patients with urinary tract infections.