{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T18:44:04Z","timestamp":1790361844181,"version":"4.1.0"},"reference-count":18,"publisher":"Microbiology Society","issue":"5","license":[{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,5,1]]},"abstract":"\n Purpose.<\/jats:bold> Antimicrobial susceptibility is slow to determine, taking several days to fully impact treatment. This proof-of-concept study assessed the feasibility of using machine-learning techniques for analysis of data produced by the flow cytometer-assisted antimicrobial susceptibility test (FAST) method we developed.<\/jats:p>\n \n Methods.<\/jats:bold> We used machine learning to assess the effect of antimicrobial agents on bacteria, comparing FAST results with broth microdilution (BMD) antimicrobial susceptibility tests (ASTs). We used \n \n Escherichia coli<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic> (1), \n \n Klebsiella pneumoniae<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic> (1) and \n \n Staphylococcus aureus<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic> (2) strains to develop the machine-learning algorithm, an expanded panel including these plus \n \n E. coli<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic> (2), \n \n K. pneumoniae<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic> (3), \n \n Proteus mirabilis<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic> (1), \n \n Pseudomonas aeruginosa<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic> (1), \n \n S. aureus<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic> (2) and \n \n Enterococcus faecalis<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic> (1), tested against FAST and BMD (Sensititre, Oxoid), then two representative isolates directly from blood cultures.<\/jats:p>\n \n Results.<\/jats:bold> Our data machines defined an antibiotic-unexposed population (AUP) of bacteria, classified the FAST result by antimicrobial concentration range, and determined a concentration-dependent antimicrobial effect (CDE) to establish a predicted inhibitory concentration (PIC). Reference strains of E. coli, K. pneumoniae<\/jats:italic> and \n \n S. aureus<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic> tested with different antimicrobial agents demonstrated concordance between BMD results and machine-learning analysis (CA, categoric agreement of 91 %; EA, essential agreement of 100 %). CA was achieved in 35 (83 %) and EA in 28 (67 %) by machine learning on first pass in a challenge panel of 27 Gram-negative and 15 Gram-positive ASTs. Same-day AST results were obtained from clinical \n \n E. coli<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic> (1) and \n \n S. aureus<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic> (1) isolates.<\/jats:p>\n \n Conclusions.<\/jats:bold> The combination of machine learning with the FAST method generated same-day AST results and has the potential to aid early antimicrobial treatment decisions, stewardship and detection of resistance.<\/jats:p>","DOI":"10.1099\/jmm.0.001092","type":"journal-article","created":{"date-parts":[[2019,10,30]],"date-time":"2019-10-30T16:12:30Z","timestamp":1572451950000},"page":"657-669","source":"Crossref","is-referenced-by-count":36,"title":["Same-day antimicrobial susceptibility test using acoustic-enhanced flow cytometry visualized with supervised machine learning"],"prefix":"10.1099","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8559-8245","authenticated-orcid":false,"given":"Timothy J. J.","family":"Inglis","sequence":"first","affiliation":[{"name":"School of Medicine, Faculty of Health and Medical Sciences, the University of Western Australia, Perth, Australia"},{"name":"Department of Microbiology, PathWest Laboratory Medicine WA, Queen Elizabeth II Medical Centre, Nedlands WA, Australia"},{"name":"The Marshall Centre for Infectious Disease Research and Training, School of Biomedical Sciences, Faculty of Health and Medical Sciences, the University of Western Australia, Perth, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8370-7284","authenticated-orcid":false,"given":"Teagan F.","family":"Paton","sequence":"additional","affiliation":[{"name":"Department of Microbiology, PathWest Laboratory Medicine WA, Queen Elizabeth II Medical Centre, Nedlands WA, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6016-8054","authenticated-orcid":false,"given":"Malgorzata K.","family":"Kopczyk","sequence":"additional","affiliation":[{"name":"Department of Microbiology, PathWest Laboratory Medicine WA, Queen Elizabeth II Medical Centre, Nedlands WA, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5036-1339","authenticated-orcid":false,"given":"Kieran T.","family":"Mulroney","sequence":"additional","affiliation":[{"name":"School of Medicine, Faculty of Health and Medical Sciences, the University of Western Australia, Perth, Australia"},{"name":"Translational Renal Research Group, Harry Perkins Institute of Medical Research, Nedlands WA 6009, Australia"},{"name":"The Marshall Centre for Infectious Disease Research and Training, School of Biomedical Sciences, Faculty of Health and Medical Sciences, the University of Western Australia, Perth, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3669-1651","authenticated-orcid":false,"given":"Christine F.","family":"Carson","sequence":"additional","affiliation":[{"name":"The Marshall Centre for Infectious Disease Research and Training, School of Biomedical Sciences, Faculty of Health and Medical Sciences, the University of Western Australia, Perth, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"345","reference":[{"key":"R1","article-title":"ISO 20776-1:2006. 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