Literature DB >> 32295889

Computer Vision and Artificial Intelligence Are Emerging Diagnostic Tools for the Clinical Microbiologist.

Daniel D Rhoads1.   

Abstract

Artificial intelligence (AI) is increasingly becoming an important component of clinical microbiology informatics. Researchers, microbiologists, laboratorians, and diagnosticians are interested in AI-based testing because these solutions have the potential to improve a test's turnaround time, quality, and cost. A study by Mathison et al. used computer vision AI (B. A. Mathison, J. L. Kohan, J. F. Walker, R. B. Smith, et al., J Clin Microbiol 58:e02053-19, 2020, https://doi.org/10.1128/JCM.02053-19), but additional opportunities for AI applications exist within the clinical microbiology laboratory. Large data sets within clinical microbiology that are amenable to the development of AI diagnostics include genomic information from isolated bacteria, metagenomic microbial findings from primary specimens, mass spectra captured from cultured bacterial isolates, and large digital images, which is the medium that Mathison et al. chose to use. AI in general and computer vision in specific are emerging tools that clinical microbiologists need to study, develop, and implement in order to improve clinical microbiology.
Copyright © 2020 American Society for Microbiology.

Keywords:  artificial intelligence; bioinformatics; computer vision; digital pathology; microbiology; parasitology

Mesh:

Year:  2020        PMID: 32295889      PMCID: PMC7269399          DOI: 10.1128/JCM.00511-20

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


  15 in total

1.  Image analysis approach for development of a decision support system for detection of malaria parasites in thin blood smear images.

Authors:  Keerthana Prasad; Jan Winter; Udayakrishna M Bhat; Raviraja V Acharya; Gopalakrishna K Prabhu
Journal:  J Digit Imaging       Date:  2012-08       Impact factor: 4.056

Review 2.  A Review of Automatic Methods Based on Image Processing Techniques for Tuberculosis Detection from Microscopic Sputum Smear Images.

Authors:  Rani Oomman Panicker; Biju Soman; Gagan Saini; Jeny Rajan
Journal:  J Med Syst       Date:  2015-10-30       Impact factor: 4.460

Review 3.  Automated antinuclear immunofluorescence antibody screening: a comparative study of six computer-aided diagnostic systems.

Authors:  Nicola Bizzaro; Antonio Antico; Stefan Platzgummer; Elio Tonutti; Danila Bassetti; Fiorenza Pesente; Renato Tozzoli; Marilina Tampoia; Danilo Villalta
Journal:  Autoimmun Rev       Date:  2013-11-09       Impact factor: 9.754

4.  Multilaboratory study of the Biomic automated well-reading instrument versus MicroScan WalkAway for reading MicroScan antimicrobial susceptibility and identification panels.

Authors:  Robert C Fader; Emily Weaver; Rhonda Fossett; Michele Toyras; John Vanderlaan; David Gibbs; Andrew Wang; Nikolaus Thierjung
Journal:  J Clin Microbiol       Date:  2013-03-13       Impact factor: 5.948

Review 5.  Clinical microbiology informatics.

Authors:  Daniel D Rhoads; Vitali Sintchenko; Carol A Rauch; Liron Pantanowitz
Journal:  Clin Microbiol Rev       Date:  2014-10       Impact factor: 26.132

Review 6.  Image analysis and machine learning for detecting malaria.

Authors:  Mahdieh Poostchi; Kamolrat Silamut; Richard J Maude; Stefan Jaeger; George Thoma
Journal:  Transl Res       Date:  2018-01-12       Impact factor: 7.012

7.  Detection of Intestinal Protozoa in Trichrome-Stained Stool Specimens by Use of a Deep Convolutional Neural Network.

Authors:  Orly Ardon; Marc Roger Couturier; Blaine A Mathison; Jessica L Kohan; John F Walker; Richard Boyd Smith
Journal:  J Clin Microbiol       Date:  2020-05-26       Impact factor: 5.948

8.  Comparison of the Copan WASPLab incorporating the BioRad expert system against the SIRscan 2000 automatic for routine antimicrobial disc diffusion susceptibility testing.

Authors:  A Cherkaoui; G Renzi; A Fischer; N Azam; D Schorderet; N Vuilleumier; J Schrenzel
Journal:  Clin Microbiol Infect       Date:  2019-11-13       Impact factor: 8.067

9.  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

10.  Automatic microscopic detection of mycobacteria in sputum: a proof-of-concept.

Authors:  D Zingue; P Weber; F Soltani; D Raoult; M Drancourt
Journal:  Sci Rep       Date:  2018-07-27       Impact factor: 4.379

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  2 in total

1.  Teleclinical Microbiology: An Innovative Approach to Providing Web-Enabled Diagnostic Laboratory Services in Syria.

Authors:  Nabil Karah; Konstantinos Antypas; Anas Al-Toutanji; Usama Suveyd; Rayane Rafei; Louis-Patrick Haraoui; Wael Elamin; Monzer Hamze; Aula Abbara; Daniel D Rhoads; Liron Pantanowitz; Bernt Eric Uhlin
Journal:  Am J Clin Pathol       Date:  2022-04-01       Impact factor: 2.493

Review 2.  REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health.

Authors:  Maryam Pishgar; Salah Fuad Issa; Margaret Sietsema; Preethi Pratap; Houshang Darabi
Journal:  Int J Environ Res Public Health       Date:  2021-06-22       Impact factor: 3.390

  2 in total

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