The first AI-based mobile application for antibiotic resistance testing
| Autor: | Alex Brunner, Clara Nordon, Ellen Marie Sebastian, Christophe Ambroise, Nada Malou, Jean-Philippe Vert, Jakub Adamek, Gabriel Dulac-Arnold, Marco Pascucci, Dena Soukieh, Mohammed-Amin Madoui, Guillaume Boniface-Chang, Franck Samson, David Aristizabal, Laetitia Blanche, Amine Bezzarga, Vincent Runge, Christian Curel, Guilhem Royer |
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| Rok vydání: | 2020 |
| Předmět: |
Susceptibility testing
medicine.diagnostic_test business.industry Computer science medicine.drug_class media_common.quotation_subject Antibiotics Gold standard (test) Antibiotic misuse Machine learning computer.software_genre Antibiotic resistance Antibiogram Reading (process) medicine Artificial intelligence business computer media_common |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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