AI-based mobile application to fight antibiotic resistance

• Published in: Nature communications Vol. 12; no. 1; p. 1173
• Main Authors: Pascucci, Marco, Royer, Guilhem, Adamek, Jakub, Asmar, Mai Al, Aristizabal, David, Blanche, Laetitia, Bezzarga, Amine, Boniface-Chang, Guillaume, Brunner, Alex, Curel, Christian, Dulac-Arnold, Gabriel, Fakhri, Rasheed M., Malou, Nada, Nordon, Clara, Runge, Vincent, Samson, Franck, Sebastian, Ellen, Soukieh, Dena, Vert, Jean-Philippe, Ambroise, Christophe, Madoui, Mohammed-Amin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19-02-2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/1564; 631/1647/794; 631/326/22/1434; 706/134; Anti-Bacterial Agents - pharmacology; Antibiotic resistance; Antibiotics; Antimicrobial agents; Antimicrobial resistance; Applications programs; Artificial Intelligence; Bacterial diseases; Bacterial Infections; Computer Science; Drug resistance; Drug Resistance, Microbial - drug effects; Expert systems; Global health; Health risks; Humanities and Social Sciences; Humans; Image Processing; Image Processing, Computer-Assisted; Machine Learning; Microbial Sensitivity Tests - methods; Mobile Applications; Mobile Computing; multidisciplinary; Public health; Reading; Science; Science (multidisciplinary); Smartphone; Smartphones; Software
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-21187-3

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 an 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.
Antimicrobial resistance is a major global health threat and its development is promoted by antibiotic misuse. Here, the authors present an offline smartphone application for automated and standardized antibiotic susceptibility testing, to be deployed in resource-limited settings.