Measurement of inhibition zone diameter in disk susceptibility tests by computerized image analysis

Gavoille, Antoine; Bardy, Béatrice; Andremont, Antoine

doi:10.1016/0010-4825(94)90014-0

Cited by 10 publications

(16 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The AIA, which is presented in this paper as an algorithm designed to estimate the diameter of the inhibition zones in disc diffusion susceptibility tests, produced relatively lower estimates than observed in previous studies which use automated system (i.e., kappa index values between 0.84 and 0.92 [16,17,19]), with 88.8% of tests showing no difference or differences lower than 4 mm (Fig. 7).…”

Section: Discussionmentioning

confidence: 56%

“…More-over, other authors who have proposed methods for the automatic detection of inhibition zones have claimed that these parameters continued to influence aspects of inhibition zone determination [17]. Gavoille et al [17] reported problems with methods that assume a continuous boundary of inhibition zones. However, their strategy, which uses the Student's t-test as an edge detection criterion, is sensitive to noise on the inhibition zones.…”

Section: Discussionmentioning

confidence: 99%

“…An earlier approach [16] presented a solution for automatic identification of inhibition zones; however, this solution did not provide strategies to avoid problems such as overlapping and non-homogeneity of the inhibition zones. Another report [17] approached this challenge through the detection of edges with respect to their texture; meanwhile, this method is based only on the saturation of pixels for locating the discs. Another proposed approach for automatic identification of inhibition zones [13] relies on the assumption that only the regions of inhibition are homogeneous, which is not always true.…”

Section: Introductionmentioning

confidence: 99%

“…The Oxoid Aura Image System [19] shows promising results; nevertheless, the techniques used are also not fully disclosed, which prevents the reproduction and application of the algorithm. Nevertheless, the main goal of these methods is to simplify and accelerate the processing of antibiograms, as well as their reading and interpretation, and to avoid variations in intra-and inter-observer readings when manually measured [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Development of an automatic identification algorithm for antibiogram analysis

Costa

Silva

Noronha

et al. 2015

Computers in Biology and Medicine

View full text Add to dashboard Cite

Routinely, diagnostic and microbiology laboratories perform antibiogram analysis which can present some difficulties leading to misreadings and intra and inter-reader deviations. An Automatic Identification Algorithm (AIA) has been proposed as a solution to overcome some issues associated with the disc diffusion method, which is the main goal of this work. AIA allows automatic scanning of inhibition zones obtained by antibiograms. More than 60 environmental isolates were tested using susceptibility tests which were performed for 12 different antibiotics for a total of 756 readings. Plate images were acquired and classified as standard or oddity. The inhibition zones were measured using the AIA and results were compared with reference method (human reading), using weighted kappa index and statistical analysis to evaluate, respectively, inter-reader agreement and correlation between AIA-based and human-based reading. Agreements were observed in 88% cases and 89% of the tests showed no difference or a o4 mm difference between AIA and human analysis, exhibiting a correlation index of 0.85 for all images, 0.90 for standards and 0.80 for oddities with no significant difference between automatic and manual method. AIA resolved some reading problems such as overlapping inhibition zones, imperfect microorganism seeding, non-homogeneity of the circumference, partial action of the antimicrobial, and formation of a second halo of inhibition. Furthermore, AIA proved to overcome some of the limitations observed in other automatic methods. Therefore, AIA may be a practical tool for automated reading of antibiograms in diagnostic and microbiology laboratories.2

show abstract

Section: Discussionmentioning

confidence: 56%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of an automatic identification algorithm for antibiogram analysis

Costa

Silva

Noronha

et al. 2015

Computers in Biology and Medicine

View full text Add to dashboard Cite

show abstract

“…Affordable solutions are few. Image processing algorithms for automatic measuring inhibition diameters have been published [16][17][18][19][20] . Among these, only AntibiogramJ 20 presents a fully functional user-friendly software, but it operates on a desktop computer onto which the images need to be previously transferred.…”

Section: Introductionmentioning

confidence: 99%

The first AI-based mobile application for antibiotic resistance testing

Pascucci

Royer

Adámek

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

AI-based mobile application to fight antibiotic resistance

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Measurement of inhibition zone diameter in disk susceptibility tests by computerized image analysis

Cited by 10 publications

References 8 publications

Development of an automatic identification algorithm for antibiogram analysis

Development of an automatic identification algorithm for antibiogram analysis

The first AI-based mobile application for antibiotic resistance testing

AI-based mobile application to fight antibiotic resistance

Contact Info

Product

Resources

About