Disc Diffusion Reader: an AI-powered potential solution to combat antibiotic resistance in developing countries
Introduction: Antimicrobial resistance (AMR) is a global health challenge, and antimicrobial susceptibility testing (AST) is vital for guiding treatment. Although widely used, the Kirby-Bauer method depends on skilled interpretation, which can be time-intensive and error-prone. This study explored...
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The Journal of Infection in Developing Countries
2025-05-01
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| Series: | Journal of Infection in Developing Countries |
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| Online Access: | https://jidc.org/index.php/journal/article/view/21108 |
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| author | Hoang B Nguyen Thanh L Phan Thi T Ung Thi KL Nguyen |
| author_facet | Hoang B Nguyen Thanh L Phan Thi T Ung Thi KL Nguyen |
| author_sort | Hoang B Nguyen |
| collection | DOAJ |
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Introduction: Antimicrobial resistance (AMR) is a global health challenge, and antimicrobial susceptibility testing (AST) is vital for guiding treatment. Although widely used, the Kirby-Bauer method depends on skilled interpretation, which can be time-intensive and error-prone. This study explored the potential of an artificial intelligence (AI)-driven progressive web app (PWA) to automate the analysis of Kirby-Bauer test images, thereby enhancing accuracy and efficiency.
Methodology: Images of Kirby-Bauer test results were annotated to train the Faster R-CNN ResNet-50 to detect agar plates, inhibition zones, and antibiotic discs. MobileNetv2 was used for antibiotic disc classification. A Human-in-the-Loop (HITL) approach enabled technicians to correct errors and improve model performance through retraining. The PWA, built with VueJS and Python-PHP, provided real-time analysis aligned with the Clinical and Laboratory Standards Institute (CLSI) and the European Committee on Antimicrobial Susceptibility Testing (EUCAST) standards.
Results: The application achieved 92.95% accuracy for inhibition zone detection and 96.92% accuracy for antibiotic disc identification, with a performance improvement of 99.28% following HITL corrections. The measurements closely aligned with those of the technicians in 89.54% of the cases. The system processed up to 50 images per hour, supporting reliable and rapid AST workflow.
Conclusions: The AI-powered “Disc Diffusion Reader” demonstrated high accuracy and efficiency, by reducing interpretation variability in the AST workflows. Its scalability and adaptability, particularly in low-resource settings, make it a valuable tool for combating AMR. Continuous retraining and validation will ensure sustained reliability, and highlight the potential of AI-driven solutions in modern microbiology.
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| format | Article |
| id | doaj-art-d30b367462184d7a9dfaa1724ba2f89f |
| institution | OA Journals |
| issn | 1972-2680 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | The Journal of Infection in Developing Countries |
| record_format | Article |
| series | Journal of Infection in Developing Countries |
| spelling | doaj-art-d30b367462184d7a9dfaa1724ba2f89f2025-08-20T02:09:51ZengThe Journal of Infection in Developing CountriesJournal of Infection in Developing Countries1972-26802025-05-01190510.3855/jidc.21108Disc Diffusion Reader: an AI-powered potential solution to combat antibiotic resistance in developing countriesHoang B Nguyen0Thanh L Phan1Thi T Ung2Thi KL Nguyen3Department of Microbiology, Hue University of Medicine and Pharmacy, Hue University, Hue City, VietnamCenter for Information Technology, Hue University of Medicine and Pharmacy, Hue University, Hue City, VietnamDepartment of Microbiology, Hue University of Medicine and Pharmacy, Hue University, Hue City, VietnamDepartment of Microbiology, Hue University of Medicine and Pharmacy, Hue University, Hue City, Vietnam Introduction: Antimicrobial resistance (AMR) is a global health challenge, and antimicrobial susceptibility testing (AST) is vital for guiding treatment. Although widely used, the Kirby-Bauer method depends on skilled interpretation, which can be time-intensive and error-prone. This study explored the potential of an artificial intelligence (AI)-driven progressive web app (PWA) to automate the analysis of Kirby-Bauer test images, thereby enhancing accuracy and efficiency. Methodology: Images of Kirby-Bauer test results were annotated to train the Faster R-CNN ResNet-50 to detect agar plates, inhibition zones, and antibiotic discs. MobileNetv2 was used for antibiotic disc classification. A Human-in-the-Loop (HITL) approach enabled technicians to correct errors and improve model performance through retraining. The PWA, built with VueJS and Python-PHP, provided real-time analysis aligned with the Clinical and Laboratory Standards Institute (CLSI) and the European Committee on Antimicrobial Susceptibility Testing (EUCAST) standards. Results: The application achieved 92.95% accuracy for inhibition zone detection and 96.92% accuracy for antibiotic disc identification, with a performance improvement of 99.28% following HITL corrections. The measurements closely aligned with those of the technicians in 89.54% of the cases. The system processed up to 50 images per hour, supporting reliable and rapid AST workflow. Conclusions: The AI-powered “Disc Diffusion Reader” demonstrated high accuracy and efficiency, by reducing interpretation variability in the AST workflows. Its scalability and adaptability, particularly in low-resource settings, make it a valuable tool for combating AMR. Continuous retraining and validation will ensure sustained reliability, and highlight the potential of AI-driven solutions in modern microbiology. https://jidc.org/index.php/journal/article/view/21108drug resistanceartificial intelligencedeveloping countriesdisc diffusion methodneural networks |
| spellingShingle | Hoang B Nguyen Thanh L Phan Thi T Ung Thi KL Nguyen Disc Diffusion Reader: an AI-powered potential solution to combat antibiotic resistance in developing countries Journal of Infection in Developing Countries drug resistance artificial intelligence developing countries disc diffusion method neural networks |
| title | Disc Diffusion Reader: an AI-powered potential solution to combat antibiotic resistance in developing countries |
| title_full | Disc Diffusion Reader: an AI-powered potential solution to combat antibiotic resistance in developing countries |
| title_fullStr | Disc Diffusion Reader: an AI-powered potential solution to combat antibiotic resistance in developing countries |
| title_full_unstemmed | Disc Diffusion Reader: an AI-powered potential solution to combat antibiotic resistance in developing countries |
| title_short | Disc Diffusion Reader: an AI-powered potential solution to combat antibiotic resistance in developing countries |
| title_sort | disc diffusion reader an ai powered potential solution to combat antibiotic resistance in developing countries |
| topic | drug resistance artificial intelligence developing countries disc diffusion method neural networks |
| url | https://jidc.org/index.php/journal/article/view/21108 |
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