A mobile based antimicrobial susceptibility testing device - prototype development and validation.

Abstract

Background and objectives Antimicrobial resistance is a major concern due to multidrug-resistant pathogens. Advances in smartphone technology provide opportunity to digitise antimicrobial susceptibility testing (AST). This study aimed to develop and validate an in-house semi-automated smartphone application (app) for the measurement and interpretation of AST. Methods In this prospective validation study, an in-house app was developed using an AI-assisted coding workflow. The app uses a digital calibration algorithm that correlates pixel dimensions with millimetres (mm). AST was performed using the Kirby-Bauer disk diffusion method (KBDD) on Escherichia coli (n=48), and Staphylococcus aureus (n=15) and zone diameters were measured manually, interpreted using Clinical and Laboratory Standards Institute (CLSI) followed by analysis of the same plates using the app. S. aureus (14 drugs) required at least two Mueller-Hinton agar (MHA) plates per isolate, while E. coli (22 drugs) required at least three MHA plates per isolate. Results AST was performed on 1,000 antibiotic disks. Zone diameters were measured and interpreted by both methods. Categorical agreement was 90.4%. Unweighted Cohen's Kappa was 0.836 (95% CI: 0.806-0.866). Categorical discrepancies occurred in 96 antibiotic disks. Very major, major, and minor error rates were 0%, 0.83%, and 9.2%, respectively. Interpretation and conclusions The app demonstrated reliable interpretation of AST, with high categorical agreement and less than 1% major error rates, supporting its safety for resistance detection. This approach offers a user-friendly alternative for busy, resource-limited settings that can be used routinely.