logo
ResearchBunny Logo
Al-based mobile application to fight antibiotic resistance

Medicine and Health

Al-based mobile application to fight antibiotic resistance

M. Pascucci, G. Royer, et al.

Discover how an innovative AI-based offline smartphone application is transforming antibiogram analysis, capturing images, guiding analysis, and providing results with impressive accuracy. Developed by an expert team including Marco Pascucci and Guilhem Royer, this tool aims to enhance antibiotic susceptibility testing in resource-limited settings.

00:00
00:00
Playback language: English
Abstract
Antimicrobial resistance is a major global health threat. This paper presents an AI-based, offline smartphone application for antibiogram analysis. The application captures images, guides the user through analysis, and provides interpreted results. Its automatic measurement procedure achieves high agreement with hospital-standard and manual systems, reducing inter-operator variability. The application is suited for resource-limited settings, increasing access to antibiotic susceptibility testing worldwide.
Publisher
NATURE COMMUNICATIONS
Published On
Feb 19, 2021
Authors
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, Mohammed-Amin Madoui
Tags
Antimicrobial resistance
AI-based application
antibiogram analysis
mobile health
resource-limited settings
antibiotic susceptibility testing

Related Publications

Explore these studies to deepen your understanding of the subject.

A deep learning approach for morphological feature extraction based on variational auto-encoder: an application to mandible shape
Biology

A deep learning approach for morphological feature extraction based on variational auto-encoder: an application to mandible shape

M. Tsutsumi, N. Saito, et al.

Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections
Medicine and Health

Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections

J. Yang, D. W. Eyre, et al.

Leveraging rail-based mobile energy storage to increase grid reliability in the face of climate uncertainty
Engineering and Technology

Leveraging rail-based mobile energy storage to increase grid reliability in the face of climate uncertainty

J. W. Moraski, N. D. Popovich, et al.

Non-antibiotic pharmaceuticals are toxic against *Escherichia coli* with no evolution of cross-resistance to antibiotics
Medicine and Health

Non-antibiotic pharmaceuticals are toxic against *Escherichia coli* with no evolution of cross-resistance to antibiotics

R. J. Hall, A. E. Snaith, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny