Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/113627
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Type: | Journal article |
Title: | Evaluation of an image analysis device (APAS) for screening urine cultures |
Author: | Glasson, J. Hill, R. Summerford, M. Giglio, S. |
Citation: | Journal of Clinical Microbiology, 2016; 54(2):300-304 |
Publisher: | American Society for Microbiology |
Issue Date: | 2016 |
ISSN: | 0095-1137 1098-660X |
Editor: | Onderdonk, A. |
Statement of Responsibility: | John Glasson, Rhys Hill, Michael Summerford, Steven Giglio |
Abstract: | While advancements have been made in some areas of pathology with diagnostic materials being screened using image analysis technologies, the reporting of cultures from agar plates remains a manual process. We compared the results for 2,163 urine cultures read by a reference panel of microbiologists, by the routine laboratory process, and by an automated plate reading system, APAS (LBT Innovations Ltd., South Australia). APAS detected colonies with a sensitivity of 99.1% and a specificity of 99.3% on blood agar, while on MacConkey agar, the colony detection sensitivity was 99.4% with a specificity of 99.3%. The device's ability to enumerate growth had an accuracy of 89.2%, and the morphological identification of colonies showed a high level of performance for the colony types typical of Escherichia coli and other enteric bacilli. On blood agar, lactose-fermenting colonies were morphologically identified with a sensitivity of 98.9%, while on MacConkey agar they were identified with a sensitivity of 99.2%. In this first clinical evaluation, APAS demonstrated high performance in the detection, enumeration, and colony classification of isolates compared with that for conventional plate-reading methods. The device found all cases reported by the laboratory and detected the most commonly encountered organisms found in urinary tract infections. |
Keywords: | Bacteriological techniques |
Rights: | Copyright © 2016 Glasson et al. This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unported license, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original author and source are credited. |
DOI: | 10.1128/JCM.02365-15 |
Published version: | http://dx.doi.org/10.1128/jcm.02365-15 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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hdl_113627.pdf | Published Version | 1.09 MB | Adobe PDF | View/Open |
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