Development of an automatic identification algorithm for antibiogram analysis

Maximally stable extremal regions-based algorithm for automatic interpretation of disc-diffusion antibiotic sensitivity test

Antibiotic resistance causes a major threat to patients suffering from infectious diseases. Accurate and timely assessment of Antibiotic Susceptibility Test (AST) is of great importance to ensure adequate treatment for patients and for epidemiological monitoring. Disc Diffusion Test (DDT) is a standard and widely used method for AST. Manual interpretation of DDT results is a tedious task and susceptible to human errors. Computer vision-based automated interpretation of DDT results will speed up the process and reduces the manpower requirement. This would assist the physician to initiate the antibiotic treatment for the patients on time and results in saving the patient's life. The crucial step in automatic interpretation of DDT result is to measure and present the diameter of zone of inhibition without manual intervention. The existing methods require manual interventions at various stages during inhibition zone diameter measurement for some typical cases. This issue is addressed in the present work through maximally stable extremal regions (MSER) based algorithm. Dataset consisting of 60 agar plate images that includes different agar medium, images having different resolution and visual quality is used to validate the proposed method. Experimental results demonstrated that there is a strong correlation between standard method and the proposed method.

AI-based mobile application to fight antibiotic resistance

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.

Antimicrobial resistance is a major global health threat and its development is promoted by antibiotic misuse. Here, the authors present an offline smartphone application for automated and standardized antibiotic susceptibility testing, to be deployed in resource-limited settings.

Antibiogramj: A tool for analysing images from disk diffusion tests

BACKGROUND AND OBJECTIVES

Disk diffusion testing, known as antibiogram, is widely applied in microbiology to determine the antimicrobial susceptibility of microorganisms. The measurement of the diameter of the zone of growth inhibition of microorganisms around the antimicrobial disks in the antibiogram is frequently performed manually by specialists using a ruler. This is a time-consuming and error-prone task that might be simplified using automated or semi-automated inhibition zone readers. However, most readers are usually expensive instruments with embedded software that require significant changes in laboratory design and workflow.

METHODS

Based on the workflow employed by specialists to determine the antimicrobial susceptibility of microorganisms, we have designed a software tool that, from images of disk diffusion tests, semi-automatises the process. Standard computer vision techniques are employed to achieve such an automatisation.

RESULTS

We present AntibiogramJ, a user-friendly and open-source software tool to semi-automatically determine, measure and categorise inhibition zones of images from disk diffusion tests. AntibiogramJ is implemented in Java and deals with images captured with any device that incorporates a camera, including digital cameras and mobile phones. The fully automatic procedure of AntibiogramJ for measuring inhibition zones achieves an overall agreement of 87% with an expert microbiologist; moreover, AntibiogramJ includes features to easily detect when the automatic reading is not correct and fix it manually to obtain the correct result.

CONCLUSIONS

AntibiogramJ is a user-friendly, platform-independent, open-source, and free tool that, up to the best of our knowledge, is the most complete software tool for antibiogram analysis without requiring any investment in new equipment or changes in the laboratory.