AI-based mobile application to fight antibiotic resistance

Marco Pascucci, Guilhem Royer, Jakub Adamek, Mai Al Asmar, David Aristizabal, Laetitia Blanche, Amine Bezzarga, Guillaume Boniface-Chang, Alex Brunner, Christian Curel, Gabriel Dulac-Arnold, Rasheed M. Fakhri, Nada Malou (), Clara Nordon, Vincent Runge, Franck Samson, Ellen Sebastian, Dena Soukieh, Jean-Philippe Vert, Christophe Ambroise () and Mohammed-Amin Madoui ()
Additional contact information
Marco Pascucci: The MSF Foundation
Guilhem Royer: Université de Paris, IAME, UMR1137, INSERM
Jakub Adamek: Google.org
Mai Al Asmar: MSF Amman Hospital
David Aristizabal: Google.org
Laetitia Blanche: The MSF Foundation
Amine Bezzarga: The MSF Foundation
Guillaume Boniface-Chang: Google.org
Alex Brunner: Google.org
Christian Curel: i2a
Gabriel Dulac-Arnold: Google Research, Brain Team
Rasheed M. Fakhri: MSF Amman Hospital
Nada Malou: The MSF Foundation
Clara Nordon: The MSF Foundation
Vincent Runge: Université Paris-Saclay, CNRS, Univ Evry, Laboratoire de Mathématiques et Modélisation d’Evry
Franck Samson: Université Paris-Saclay, CNRS, Univ Evry, Laboratoire de Mathématiques et Modélisation d’Evry
Ellen Sebastian: Google.org
Dena Soukieh: Google.org
Jean-Philippe Vert: Google Research, Brain Team
Christophe Ambroise: Université Paris-Saclay, CNRS, Univ Evry, Laboratoire de Mathématiques et Modélisation d’Evry
Mohammed-Amin Madoui: Université Paris-Saclay, Univ Evry, CNRS, CEA, Génomique métabolique

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: 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 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.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-021-21187-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21187-3

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-21187-3

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().