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AI-based mobile application to fight antibiotic resistance

Marco Pascucci
Guilhem Royer
Jakub Adámek
Mai Al Asmar
David Aristizabal
Laetitia Blanche
Amine Bezzarga
Guillaume Boniface-Chang
Alex Brunner
Christian Curel
Rasheed M. Fakhri
Nada Malou
Clara Nordon
Vincent Runge
Franck Samson
Ellen Marie Sebastian
Dena Soukieh
Jean-Philippe Vert
Christophe Ambroise
Mohammed-Amin Madoui
Nature Communications, vol. 12 (2021), pp. 1173


Antimicrobial resistance is a major global health threat and its development is promoted by antibiotic misuse. While disk diffusion antibiotic susceptibility testing (AST, also called antibiogram) is broadly used to test for antibiotic resistance in bacterial infections, it faces strong criticism because of inter-operator variability and the complexity of interpretative reading. Automatic reading systems address these issues, but are not always adapted or available to resource-limited settings. We present NewAppName, the first artificial intelligence (AI)-based, offline smartphone application for antibiogram analysis. NewAppName captures images with the phone’s camera, and the user is guided throughout the analysis on the same device by a user-friendly graphical interface. An embedded expert system validates the coherence of the antibiogram data and provides interpreted results. The fully automatic measurement procedure of NewAppName’s reading system achieves an overall agreement of 90 % on susceptibility categorization against a hospital-standard automatic system and 98 % against manual measurement (gold standard), with reduced inter-operator variability. NewAppName performance showed that the automatic reading of antibiotic resistance testing is entirely feasible on a smartphone. NewAppName is suited for resource-limited settings, and therefore has the potential to significantly increase patients’ access to AST worldwide.