AI-based mobile application to fight antibiotic resistance - Université Paris-Est-Créteil-Val-de-Marne
Article Dans Une Revue Nature Communications Année : 2021

AI-based mobile application to fight antibiotic resistance

Marco Pascucci
Guilhem Royer
Jakub Adamek
  • Fonction : Auteur
Mai Al Asmar
  • Fonction : Auteur
David Aristizabal
  • Fonction : Auteur
Laetitia Blanche
  • Fonction : Auteur
Amine Bezzarga
  • Fonction : Auteur
Guillaume Boniface-Chang
  • Fonction : Auteur
Alex Brunner
  • Fonction : Auteur
Christian Curel
  • Fonction : Auteur
Gabriel Dulac-Arnold
  • Fonction : Auteur
Rasheed Fakhri
  • Fonction : Auteur
Nada Malou
  • Fonction : Auteur
Clara Nordon
  • Fonction : Auteur
Vincent Runge
  • Fonction : Auteur
  • PersonId : 1352490
Franck Samson
Ellen Sebastian
  • Fonction : Auteur
Dena Soukieh
  • Fonction : Auteur
Jean-Philippe Vert
  • Fonction : Auteur
Christophe Ambroise
Mohammed-Amin Madoui

Résumé

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.
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Dates et versions

hal-04283546 , version 1 (31-01-2024)

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Marco Pascucci, Guilhem Royer, Jakub Adamek, Mai Al Asmar, David Aristizabal, et al.. AI-based mobile application to fight antibiotic resistance. Nature Communications, 2021, 12 (1), pp.1173. ⟨10.1038/s41467-021-21187-3⟩. ⟨hal-04283546⟩
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