Automated Detection of Streptococcus pyogenes Pharyngitis by Use of Colorex Strep A CHROMagar and WASPLab Artificial Intelligence Chromogenic Detection Module Software

Van, Tam T.; Mata, Kenneth; Bard, Jennifer Dien

doi:10.1128/jcm.00811-19

Cited by 40 publications

(20 citation statements)

References 10 publications

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“…For example, the software can segregate urine culture plates according to whether they show growth, no growth, or insignificant growth, and the color detection module (CDM) can recognize and differentiate colony colors on chromogenic agars. Previous studies have described the ability of the PM CDM software to increase the sensitivity and specificity of detection of methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), and group A Streptococcus using chromogenic media ( 12 – 14 ). PM software is designed to be an extension of the WASPLab.…”

Section: Introductionmentioning

confidence: 99%

Digital Image Analysis for the Detection of Group B Streptococcus from ChromID Strepto B Medium Using PhenoMatrix Algorithms

et al. 2020

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Group B Streptococcus (GBS) can be found to colonize about 25% of all healthy, adult women and is the leading infectious cause of early neonatal morbidity and mortality in the United States. This study evaluates the clinical performance of PhenoMatrix (PM) Chromogenic Detection Module (CDM) digital imaging software in detection of GBS from LIM broth plated on CHROMID® Strepto B Chromogenic media (ChromID) using the WASP automated processor. The performance of the PM CDM was compared to manual culture review of the digital images and molecular detection of GBS. ChromID alone had a sensitivity and specificity of 84.5% and 94.7%, respectively, after 48 hours as compared to nucleic acid amplification testing (NAAT). As compared to the composite reference for positivity, when PM CDM was used to detect GBS from ChromID the sensitivity was 100% with no true positive GBS isolates missed by 48 hours of incubation. Overall, evaluating all three methods for the detection of GBS, the sensitivities of NAAT, ChromID plus PM CDM at 48 hours, and ChromID alone at 48 hours were 96.8%, 95.5% and 90.3%, respectively. The specificities of NAAT, ChromID plus PM CDM and ChromID alone were 97.7%, 63.0%, and 95.4%, respectively. The use of ChromGBS in combination with the PM CDM was similar to the sensitivity of molecular detection. Further, the algorithm never called a culture negative that was determined to be positive by manual reading and identified an additional eight true positive specimens that were missed by manual digital image culture reading.

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Section: Introductionmentioning

confidence: 99%

Digital Image Analysis for the Detection of Group B Streptococcus from ChromID Strepto B Medium Using PhenoMatrix Algorithms

et al. 2020

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“…Table 1 shows a partial listing of body fluids and secretions that can be deposited on or acquired from fingerprint scanners, alongside some of their commonly associated pathogenic isolates. Unconfirmed pathogenic association with tears, although chronic HBV has been found in the tears of children [29] Tears SARS-CoV-2 [30,31], filoviruses [32,33], HBV [34], HSV b [35], Escherichia coli [36] Saliva SARS-CoV-2 [37], filoviruses [33], Streptococcus [38], Haemophilus influenzae [39,40], parainfluenza [41,42], Mirabella catarrhal [43], Actinomyces [44,45], Mycoplasma pneumoniae [46] Nasal mucus fluid Staphylococcus [26], Klebsiella species [47][48][49], Pseudomonas species [50], E coli [36], Proteus species [51] Pus SARS-CoV-2 [52], filoviruses [33], HBV [26,28] Hemoglobin/blood E coli [53], hepatitis C [54,55], HSV [56], SARS-CoV-2 [57,58], Zika virus [59][60][61], Ureaplasma urealyticum [62,63], Enterococcus faecalis [64], alpha-hemolytic str...…”

Section: Possible Disease Spread Via Pathogenic Deposits On Fingerprimentioning

confidence: 99%

Fingerprint Biometric System Hygiene and the Risk of COVID-19 Transmission

Okereafor¹,

Ekong²,

Markson³

et al. 2020

JMIR Biomed Eng

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Biometric systems use scanners to verify the identity of human beings by measuring the patterns of their behavioral or physiological characteristics. Some biometric systems are contactless and do not require direct touch to perform these measurements; others, such as fingerprint verification systems, require the user to make direct physical contact with the scanner for a specified duration for the biometric pattern of the user to be properly read and measured. This may increase the possibility of contamination with harmful microbial pathogens or of cross-contamination of food and water by subsequent users. Physical contact also increases the likelihood of inoculation of harmful microbial pathogens into the respiratory tract, thereby triggering infectious diseases. In this viewpoint, we establish the likelihood of infectious disease transmission through touch-based fingerprint biometric devices and discuss control measures to curb the spread of infectious diseases, including COVID-19.

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“…Artificial intelligence software has also been employed to automate the process of examining throat cultures to identify GABHS. Automatic detection of GABHS produces results that are superior to classification decisions made by lab technicians, improving diagnostic accuracy (Van et al, 2019).…”

Section: Machine Learning and Artificial Intelligencementioning

confidence: 99%

Diagnostic Methods, Clinical Guidelines, and Antibiotic Treatment for Group A Streptococcal Pharyngitis: A Narrative Review

Mustafa

Ghaffari

2020

Front. Cell. Infect. Microbiol.

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The most common bacterial cause of pharyngitis is infection by Group A b-hemolytic streptococcus (GABHS), commonly known as strep throat. 5-15% of adults and 15-35% of children in the United States with pharyngitis have a GABHS infection. The symptoms of GABHS overlap with non-GABHS and viral causes of acute pharyngitis, complicating the problem of diagnosis. A careful physical examination and patient history is the starting point for diagnosing GABHS. After a physical examination and patient history is completed, five types of diagnostic methods can be used to ascertain the presence of a GABHS infection: clinical scoring systems, rapid antigen detection tests, throat culture, nucleic acid amplification tests, and machine learning and artificial intelligence. Clinical guidelines developed by professional associations can help medical professionals choose among available techniques to diagnose strep throat. However, guidelines for diagnosing GABHS created by the American and European professional associations vary significantly, and there is substantial evidence that most physicians do not follow any published guidelines. Treatment for GABHS using analgesics, antipyretics, and antibiotics seeks to provide symptom relief, shorten the duration of illness, prevent nonsuppurative and suppurative complications, and decrease the risk of contagion, while minimizing the unnecessary use of antibiotics. There is broad agreement that antibiotics with narrow spectrums of activity are appropriate for treating strep throat. But whether and when patients should be treated with antibiotics for GABHS remains a controversial question. There is no clearly superior management strategy for strep throat, as significant controversy exists regarding the best methods to diagnose GABHS and under what conditions antibiotics should be prescribed.

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Automated Detection of Streptococcus pyogenes Pharyngitis by Use of Colorex Strep A CHROMagar and WASPLab Artificial Intelligence Chromogenic Detection Module Software

Cited by 40 publications

References 10 publications

Digital Image Analysis for the Detection of Group B Streptococcus from ChromID Strepto B Medium Using PhenoMatrix Algorithms

Digital Image Analysis for the Detection of Group B Streptococcus from ChromID Strepto B Medium Using PhenoMatrix Algorithms

Fingerprint Biometric System Hygiene and the Risk of COVID-19 Transmission

Diagnostic Methods, Clinical Guidelines, and Antibiotic Treatment for Group A Streptococcal Pharyngitis: A Narrative Review

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