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AI-based mobile application to fight antibiotic resistance.

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.

SUBMITTER: Pascucci M

PROVIDER: S-EPMC7895972 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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AI-based mobile application to fight antibiotic resistance.

Pascucci Marco M Royer Guilhem G Adamek Jakub J Asmar Mai Al MA Aristizabal David D Blanche Laetitia L Bezzarga Amine A Boniface-Chang Guillaume G Brunner Alex A Curel Christian C Dulac-Arnold Gabriel G Fakhri Rasheed M RM Malou Nada N Nordon Clara C Runge Vincent V Samson Franck F Sebastian Ellen E Soukieh Dena D Vert Jean-Philippe JP Ambroise Christophe C Madoui Mohammed-Amin MA

Nature communications 20210219 1

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. ...[more]

PMID: 33608509

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Project description:The reversibility of antibiotic resistance is theoretically attractive due to the prospect of restoring the clinical potency of antibiotics. It is important to find out the factors that affect the reversibility of antibiotic resistance. Here, an mcr-1-positive multidrug-resistant (MDR) environmental Escherichia coli isolate was successively passaged under four antibiotic-free culture conditions. The relative abundances of multiple antibiotic resistance genes (ARGs) kept decreasing during the successive passages. The linear correlations between abundances of ARGs on the same MDR plasmid reflected that the decay of antibiotic resistance during the passage was mainly due to the elimination of the MDR plasmid (pMCR_W5-6). Colistin-susceptible strains were isolated at the end of the passage. The whole-genome sequencing of two susceptible isolates detected the elimination of the MDR plasmid and deletion of the mcr-1 gene. Deletions of DNA fragments from chromosome and plasmid were closely related to a variety of insertion sequences (ISs). The results of coculture of resistant and susceptible strains at different antibiotic concentrations indicated that the high fitness cost led to the poor stability of mobile ARGs. Strict control of the use of antibiotics can at least reverse the severe antibiotic resistance caused by mobile ARGs of high fitness cost. IMPORTANCE The dissemination of bacterial antibiotic resistance is a serious threat to human health. The development of new antibiotics faces both economic and technological challenges. The reversibility of antibiotic resistance has become an important issue causing wide concern due to the prospect of restoring the clinical potency of antibiotics. Our study suggests that the high mobility of ARGs of high fitness cost may just reflect their poor stability. Therefore, strict control of the use of antibiotics can at least reverse the severe antibiotic resistance caused by mobile ARGs of high fitness cost. This study brings hope for the possibility of curbing the dissemination of antibiotic resistance.

Project description:Using on-farm microbiological culture (OFC), based on chromogenic culture media, enables the identification of mastitis causing pathogens in about 24 h, allows rapid decision making on selective treatment and control management measures of cows with clinical mastitis (CM). However, accurate interpretation of OFC results requires trained and experienced operators, which could be a limitation for the use of OFC in dairy farms. Our hypothesis was that AI-based automated plate reading mobile application can analyze images of microorganisms' colonies in chromogenic culture media with similar diagnostic performance as a trained specialist evaluator. Therefore, the aim of the present study was to evaluate the diagnostic accuracy of an AI-based application (Rumi; OnFarm, Piracicaba, São Paulo, Brazil) for interpreting images of mastitis causing microorganism colonies grown in chromogenic culture media. For this study two trials were organized to compare the results obtained using an AI-based application Rumi with the interpretation of: (1) a trained specialist, using MALDI-TOF MS as the gold standard; (2) farm personnel users (FPU). In trial 1, a total of 476 CM milk samples, from 11 farms located in São Paulo (n = 7) and Minas Gerais (n = 4), southeast Brazil, were inoculated in chromogenic culture media plates (Smartcolor 2, OnFarm, Piracicaba, São Paulo, Brazil) by specialists under lab conditions, and digital images were recorded 24 h after incubation at 37 °C. After that, all the 476 digital images were analyzed by the Rumi and by another specialist (who only had access to the digital images) and the diagnostic accuracy indicators sensitivity (Se) and specificity (Sp) were calculated using MALDI-TOF MS microbiological identification of the isolates as the reference. In Trial 2, a total of 208 CM milk samples, from 150 farms from Brazil, were inoculated in chromogenic culture media plates by FPU, and the results of microbiological growth were visually interpreted by FPU under on-farm conditions. After visual interpretation, results were recorded using an OnFarmApp application (herd manage application for mastitis by OnFarm, Piracicaba, São Paulo, Brazil), and the images of the chromogenic culture plates were captured by the OnFarmApp to be evaluated by Rumi and Bayesian Latent Class Models were performed to compare Rumi and the FPU. In Trial 1, Rumi presented high and intermediate accuracy results, with the only exception of the low Enterococcus spp.'s Se. In comparison with the specialist, Rumi performed similarly in Se and Sp for most groups of pathogens, with the only exception of non-aureus staphylococci where Se results were lower. Both Rumi and the specialist achieved Sp results > 0.96. In Trial 2, Rumi had similar results as the FPU in the Bayesian Latent Class Model analysis. In conclusion, the use of the AI-based automated plate reading mobile application can be an alternative for visual interpretation of OFC results, simplifying the procedures for selective treatment decisions for CM based on OFC.

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AI-based mobile application to fight antibiotic resistance.

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AI-based mobile application to fight antibiotic resistance.

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