The first AI-based mobile application for antibiotic resistance testing

• Published in: bioRxiv
• Main Authors: Pascucci, Marco, Royer, Guilhem, Adamek, Jakub, Aristizabal, David, Blanche, Laetitia, Bezzarga, Amine, Boniface-Chang, Guillaume, Brunner, Alex, Curel, Christian, Dulac-Arnold, Gabriel, Malou, Nada, Nordon, Clara, Runge, Vincent, Samson, Franck, Sebastian, Ellen, Soukieh, Dena, Vert, Jean-Philippe, Ambroise, Christophe, Mohammed-Amin Madoui
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 30.11.2020
• Subjects: Antibiotic resistance; Antibiotics; Antimicrobial resistance; Artificial intelligence; Drug resistance; Reading; Smartphones
• DOI: 10.1101/2020.07.23.216929

ABSTRACT 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 the first artificial intelligence (AI)-based, offline smartphone application for antibiogram analysis. The application 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 our application’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. The application’s performance showed that the automatic reading of antibiotic resistance testing is entirely feasible on a smartphone. Moreover our application is suited for resource-limited settings, and therefore has the potential to significantly increase patients’ access to AST worldwide. Competing Interest Statement The authors have declared no competing interest. Footnotes * The structure of the manuscript has been improved and data and software resources are properly referenced. Figures captions have been rewritten. * ↵VIII https://mpascucci.github.io/AST-image-processing * ↵IX https://mpascucci.github.io/ASTapp-protocol