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Literature DB >> 32024725

Machine Learning Takes Laboratory Automation to the Next Level.

Abstract

Clinical microbiology laboratories face challenges with workload and understaffing that other clinical laboratory sections have addressed with automation. In this issue of the Journal of Clinical Microbiology, M. L. Faron, B. W. Buchan, R. F. Relich, J. Clark, and N. A. Ledeboer (J Clin Microbiol 58:e01683-19, 2020, https://doi.org/10.1128/JCM.01683-19) evaluate the performance of automated image analysis software to screen urine cultures for further workup according to their total number of CFU. Urine cultures are the highest volume specimen type for most laboratories, so this software has the potential for tremendous gains in laboratory efficiency and quality due to the consistency of colony quantification.

Mesh：

Year: 2020 PMID： 32024725 PMCID： PMC7098768 DOI： 10.1128/JCM.00012-20

Source DB: PubMed Journal: J Clin Microbiol ISSN： 0095-1137 Impact factor: 5.948

15 in total

Review 1. Machine Learning in Medicine.

Authors: Alvin Rajkomar; Jeffrey Dean; Isaac Kohane
Journal: N Engl J Med Date: 2019-04-04 Impact factor: 91.245

2. Automatic Digital Analysis of Chromogenic Media for Vancomycin-Resistant-Enterococcus Screens Using Copan WASPLab.

Authors: Matthew L Faron; Blake W Buchan; Christopher Coon; Theo Liebregts; Anita van Bree; Arjan R Jansz; Genevieve Soucy; John Korver; Nathan A Ledeboer
Journal: J Clin Microbiol Date: 2016-07-13 Impact factor: 5.948

Review 3. Total Laboratory Automation: What Is Gained, What Is Lost, and Who Can Afford It?

Authors: Richard B Thomson; Erin McElvania
Journal: Clin Lab Med Date: 2019-09 Impact factor: 1.935

4. Total Laboratory Automation in Clinical Microbiology: a Micro-Comic Strip.

Authors: Alexander J McAdam
Journal: J Clin Microbiol Date: 2018-03-26 Impact factor: 5.948

5. Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts.

Authors: Guy Nir; Soheil Hor; Davood Karimi; Ladan Fazli; Brian F Skinnider; Peyman Tavassoli; Dmitry Turbin; Carlos F Villamil; Gang Wang; R Storey Wilson; Kenneth A Iczkowski; M Scott Lucia; Peter C Black; Purang Abolmaesumi; S Larry Goldenberg; Septimiu E Salcudean
Journal: Med Image Anal Date: 2018-09-24 Impact factor: 8.545

6. The American Society for Clinical Pathology's 2016-2017 Vacancy Survey of Medical Laboratories in the United States.

Authors: Edna Garcia; Iman Kundu; Asma Ali; Ryan Soles
Journal: Am J Clin Pathol Date: 2018-03-29 Impact factor: 2.493

7. Automated Scoring of Chromogenic Media for Detection of Methicillin-Resistant Staphylococcus aureus by Use of WASPLab Image Analysis Software.

Authors: Matthew L Faron; Blake W Buchan; Chiara Vismara; Carla Lacchini; Alessandra Bielli; Giovanni Gesu; Theo Liebregts; Anita van Bree; Arjan Jansz; Genevieve Soucy; John Korver; Nathan A Ledeboer
Journal: J Clin Microbiol Date: 2015-12-30 Impact factor: 5.948

8. Observations on Variations in Manual Reading of Cultures.

Authors: John Glasson; Rhys Hill; Michael Summerford; Steven Giglio
Journal: J Clin Microbiol Date: 2016-08-31 Impact factor: 5.948

9. Performance of Kiestra total laboratory automation combined with MS in clinical microbiology practice.

Authors: Nico T Mutters; Caspar J Hodiamont; Menno D de Jong; Hendri P J Overmeijer; Mandy van den Boogaard; Caroline E Visser
Journal: Ann Lab Med Date: 2014-02-13 Impact factor: 3.464

7 in total

1. Laboratory Automation in the Microbiology Laboratory: an Ongoing Journey, Not a Tale?

Authors: Stefan Zimmermann
Journal: J Clin Microbiol Date: 2021-02-18 Impact factor: 5.948

2. Validation of HER2 Status in Whole Genome Sequencing Data of Breast Cancers with the Ploidy-Corrected Copy Number Approach.

Authors: Marzena Wojtaszewska; Rafał Stępień; Alicja Woźna; Maciej Piernik; Pawel Sztromwasser; Maciej Dąbrowski; Michał Gniot; Sławomir Szymański; Maciej Socha; Piotr Kasprzak; Rafał Matkowski; Paweł Zawadzki
Journal: Mol Diagn Ther Date: 2021-12-21 Impact factor: 4.074

3. Total Laboratory Automation and Three Shifts Reduce Turnaround Time of Cerebrospinal Fluid Culture Results in the Chinese Clinical Microbiology Laboratory.

Authors: Weili Zhang; Siying Wu; Jin Deng; Quanfeng Liao; Ya Liu; Li Xiong; Ling Shu; Yu Yuan; Yuling Xiao; Ying Ma; Mei Kang; Dongdong Li; Yi Xie
Journal: Front Cell Infect Microbiol Date: 2021-12-02 Impact factor: 5.293

Review 4. Total Laboratory Automation for Rapid Detection and Identification of Microorganisms and Their Antimicrobial Resistance Profiles.

Authors: Abdessalam Cherkaoui; Jacques Schrenzel
Journal: Front Cell Infect Microbiol Date: 2022-02-03 Impact factor: 5.293

5. Microbiology 2.0-A "behind the scenes" consideration for artificial intelligence applications for interpretive culture plate reading in routine diagnostic laboratories.

Authors: B DeYoung; M Morales; S Giglio
Journal: Front Microbiol Date: 2022-08-04 Impact factor: 6.064

Review 6. Clinlabomics: leveraging clinical laboratory data by data mining strategies.

Authors: Xiaoxia Wen; Ping Leng; Jiasi Wang; Guishu Yang; Ruiling Zu; Xiaojiong Jia; Kaijiong Zhang; Birga Anteneh Mengesha; Jian Huang; Dongsheng Wang; Huaichao Luo
Journal: BMC Bioinformatics Date: 2022-09-24 Impact factor: 3.307

7. Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study.

Authors: Alexios-Fotios A Mentis; Irene Garcia; Juan Jiménez; Maria Paparoupa; Athanasia Xirogianni; Anastasia Papandreou; Georgina Tzanakaki
Journal: Diagnostics (Basel) Date: 2021-03-28

7 in total