Artificial Intelligence Applications In Clinical Microbiology Laboratory

Abstract

Artificial intelligence is becoming an increasingly important component of clinical microbiology informatics. Researchers, microbiologists, laboratorians, and diagnosticians are interested in AI-based testing because these applications have the potential to improve the turnaround time, quality, and cost of a test. Artificial intelligence which has gained importance in the laboratory, is used to support decision-making, identification and antimicrobial susceptibility testing with various technologies, image analyses, and MALDI-TOF-MS in medical microbiology and in infectious disease testing. Treatment of infections requires rapid and accurate identification and antimicrobial susceptibility testing. Modern artificial intelligence (AI) and machine-learning (ML) methods can now complete tasks with performance characteristic comparable to those of expert human operators. As a result, many healthcare fields combine these technologies, including in vitro diagnostics and, more broadly laboratory medicine, incorporate these technologies. These technologies are rapidly being developed and disclosed, but by comparison, their application so far has been limited. We need to further establish best practices and improve our information system and communications infrastructure to promote the implementation of reliable and advanced machine learning-based technologies. İnvolvement of the clinical microbiology laboratory community is essential to ensure that laboratory data is adequately accessible and thoughtfully incorporated into robust, safe and clinically effective ML-supported clinical diagnoses and such technological adjustments will lead to future breakthroughs in microbiology laboratories.

Keywords

• Microbiology
• Artificial intelligence
• Machine-learning

Ethical Statement

Since this study was a review study, Ethics committee approval was not received.

Klinik Mikrobiyoloji Laboratuvarinda Yapay Zeka Uygulamaları

Abstract

Yapay zeka, klinik mikrobiyoloji bilişiminin giderek daha önemli bir bileşeni haline gelmektedir. Araştırmacılar, mikrobiyologlar, laboratuvar uzmanları ve tanı-teşhis uzmanları yapay zeka tabanlı testlerle ilgileniyor çünkü bu uygulamalar bir testin geri dönüş süresini, kalitesini ve maliyetini iyileştirme potansiyeline sahiptir. Laboratuvarda önem kazanan yapay zeka; tıbbi mikrobiyoloji ve enfeksiyon hastalıkları testlerinde çeşitli teknolojiler, görüntü analizleri ve MALDI-TOF-MS ile karar verme, tanımlama ve antimikrobiyal duyarlılık testlerini desteklemek amacıyla kullanılmaktadır. Enfeksiyonların tedavisi hızlı ve doğru tanımlamayı ve antimikrobiyal duyarlılık testini gerektirir. Modern yapay zeka (AI) ve makine öğrenimi (ML) yöntemleri görevlerini, uzman insan operatörlerin performansıyla karşılaştırılabilecek performans özellikleriyle tamamlayabiliyor. Sonuç olarak, birçok sağlık alanı, in vitro teşhisler de dahil olmak üzere bu teknolojileri birleştirir ve daha geniş anlamda laboratuvar tıbbı bu teknolojileri içerir. Bu teknolojiler hızla geliştirilmekte ve açıklanmaktadır, ancak kıyaslandığında şu ana kadarki uygulamalar sınırlıdır. Güvenilir ve gelişmiş makine öğrenimi tabanlı teknolojilerin uygulanmasını teşvik etmek için en iyi uygulamaları daha fazla oluşturmamız, bilgi sistemimizi ve iletişim altyapımızı geliştirmemiz gerekiyor. Klinik mikrobiyoloji laboratuvar topluluğunun katılımı, laboratuvar verilerinin yeterince erişilebilir olmasını, sağlam, güvenli ve klinik olarak etkili ML destekli klinik teşhislere ve bu tür uygulamalara dikkatli bir şekilde dahil edilmesini sağlamak için esastır ve bu tip teknolojik düzenlemeler mikrobiyoloji laboratuvarlarında gelecekte çığır açacaktır.

Keywords

• Mikrobiyoloji
• Yapay zeka
• Makine öğrenimi

References

1. Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, Liu X, Wu Y, Dong F, Qiu CW, Qiu J, Hua K, Su W, Wu J, Xu H, Han Y, Fu C, Yin Z, Liu M, Roepman R, Dietmann S, Virta M, Kengara F, Zhang Z, Zhang L, Zhao T, Dai J, Yang J, Lan L, Luo M, Liu Z, An T, Zhang B, He X, Cong S, Liu X, Zhang W, Lewis JP, Tiedje JM, Wang Q, An Z, Wang F, Zhang L, Huang T, Lu C, Cai Z, Wang F, Zhang J. Artificial intelligence: A powerful paradigm for scientific research. Innova-tion (Camb). 2021 Oct 28;2(4):100179. doi: 10.1016/j.xinn.2021.100179. PMID: 34877560; PMCID: PMC8633405.
2. Abernethy A, Adams L, Barrett M, Bechtel C, Brennan P, Butte A, Faulkner J, Fontaine E, Friedhoff S, Halamka J, Howell M, Johnson K, Long P, McGraw D, Miller R, Lee P, Perlin J, Rucker D, Sandy L, Savage L, Stump L, Tang P, Topol E, Tuckson R, Valdes K. The Promise of Digital Health: Then, Now, and the Future. NAM Perspect. 2022 Jun 27;2022:10.31478/202206e. doi: 10.31478/202206e. PMID: 36177208; PMCID: PMC9499383.
3. Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L. Review of deep learning: concepts, CNN architectures, challen-ges, applications, future directions. J Big Data. 2021;8(1):53. doi: 10.1186/s40537-021-00444-8. Epub 2021 Mar 31. PMID: 33816053; PMCID: PMC8010506.
4. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021 Jul;8(2):e188-e194. doi: 10.7861/fhj.2021-0095. PMID: 34286183; PMCID: PMC8285156.
5. Newman-Toker DE. Where Is the "Low-Hanging Fruit" in Diagnostic Quality and Safety? Qual Manag Health Care. 2018 Oct/Dec;27(4):234-236. doi: 10.1097/QMH.0000000000000184. PMID: 30260932
6. Smith KP, Kirby JE. Image analysis and artificial intelligence in infectious disease diagnostics. Clin Microbiol Infect. 2020 Oct;26(10):1318-1323. doi: 10.1016/j.cmi.2020.03.012. Epub 2020 Mar 22. PMID: 32213317; PMCID: PMC7508855.
7. Andras JP, Fields PD, Du Pasquier L, Fredericksen M, Ebert D. Genome-Wide Association Analysis Identifies a Genetic Basis of Infectivity in a Model Bacterial Pathogen. Mol Biol Evol. 2020 Dec 16;37(12):3439-3452. doi: 10.1093/molbev/msaa173. PMID: 32658956; PMCID: PMC7743900.
8. de Mello BH, Rigo SJ, da Costa CA, da Rosa Righi R, Donida B, Bez MR, Schunke LC. Seman-tic interoperability in health records standards: a systematic literature review. Health Technol (Berl). 2022;12(2):255-272. doi: 10.1007/s12553-022-00639-w. Epub 2022 Jan 26. PMID: 35103230; PMCID: PMC8791650.

9. Bhardwaj A, Kishore S, Pandey DK. Artificial Intelligence in Biological Sciences. Life (Basel). 2022 Sep 14;12(9):1430. doi: 10.3390/life12091430. PMID: 36143468; PMCID: PMC9505413.
10. Armstrong GL, MacCannell DR, Taylor J, Carleton HA, Neuhaus EB, Bradbury RS, Posey JE, Gwinn M. Pathogen Genomics in Public Health. N Engl J Med. 2019 Dec 26;381(26):2569-2580. doi: 10.1056/NEJMsr1813907. PMID: 31881145; PMCID: PMC7008580.
11. Aschbacher R, Pagani L, Migliavacca R, Pagani L; GLISTer (Gruppo di Lavoro per lo Studio delle Infezioni nelle Residenze Sanitarie Assistite e Strutture Assimilabili) working group. Re-commendations for the surveillance of multidrug-resistant bacteria in Italian long-term care facilities by the GLISTer working group of the Italian Association of Clinical Microbiologists (AMCLI). Antimicrob Resist Infect Control. 2020 Jul 13;9(1):106. doi: 10.1186/s13756-020-00771-0. PMID: 32660605; PMCID: PMC7356128.
12. Awad, M.M.; Hashem, A.; Naguib, H.M. The Impact of Lean Management Practices on Eco-nomic Sustainability in Services Sector. Sustainability 2022, 14, 9323. https://doi.org/10.3390/su14159323
13. Marcos-Zambrano LJ, Karaduzovic-Hadziabdic K, Loncar Turukalo T, Przymus P, Trajkovik V, Aasmets O, Berland M, Gruca A, Hasic J, Hron K, Klammsteiner T, Kolev M, Lahti L, Lo-pes MB, Moreno V, Naskinova I, Org E, Paciência I, Papoutsoglou G, Shigdel R, Stres B, Vilne B, Yousef M, Zdravevski E, Tsamardinos I, Carrillo de Santa Pau E, Claesson MJ, Moreno-Indias I, Truu J. Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment. Front Mic-robiol. 2021 Feb 19;12:634511. doi: 10.3389/fmicb.2021.634511. PMID: 33737920; PMCID: PMC7962872.
14. Guney G, Yigin BO, Guven N, Alici YH, Colak B, Erzin G, Saygili G. An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry. Clin Psychopharmacol Neu-rosci. 2021 May 31;19(2):206-219. doi: 10.9758/cpn.2021.19.2.206. PMID: 33888650; PMCID: PMC8077051.
15. Tobore I, Li J, Yuhang L, Al-Handarish Y, Kandwal A, Nie Z, Wang L. Deep Learning Inter-vention for Health Care Challenges: Some Biomedical Domain Considerations. JMIR Mhealth Uhealth. 2019 Aug 2;7(8):e11966. doi: 10.2196/11966. PMID: 31376272; PMCID: PMC6696854.
16. Institute of Medicine (US) Committee on Health Research and the Privacy of Health Informa-tion: The HIPAA Privacy Rule; Nass SJ, Levit LA, Gostin LO, editors. Beyond the HIPAA Pri-vacy Rule: Enhancing Privacy, Improving Health Through Research. Washington (DC): Natio-nal Academies Press (US); 2009. 2, The Value and Importance of Health Information Pri-vacy. Available from: https://www.ncbi.nlm.nih.gov/books/NBK9579/
17. Burckhardt I. Laboratory Automation in Clinical Microbiology. Bioengineering (Basel). 2018 Nov 22;5(4):102. doi: 10.3390/bioengineering5040102. PMID: 30467275; PMCID: PMC6315553.
18. Liu Q, Jin X, Cheng J, Zhou H, Zhang Y, Dai Y. Advances in the application of molecular diag-nostic techniques for the detection of infectious disease pathogens (Review). Mol Med Rep. 2023 May;27(5):104. doi: 10.3892/mmr.2023.12991. Epub 2023 Apr 7. PMID: 37026505; PMCID: PMC10086565.
19. Rhoads DD, Sintchenko V, Rauch CA, Pantanowitz L. Clinical microbiology informatics. Clin Microbiol Rev. 2014 Oct;27(4):1025-47. doi: 10.1128/CMR.00049-14. PMID: 25278581; PMCID: PMC4187636.
20. Leo S, Cherkaoui A, Renzi G, Schrenzel J. Mini Review: Clinical Routine Microbiology in the Era of Automation and Digital Health. Front Cell Infect Microbiol. 2020 Nov 30;10:582028. doi: 10.3389/fcimb.2020.582028. PMID: 33330127; PMCID: PMC7734209.
21. Cherkaoui A, Schrenzel J. Total Laboratory Automation for Rapid Detection and Identification of Microorganisms and Their Antimicrobial Resistance Profiles. Front Cell Infect Microbiol. 2022 Feb 3;12:807668. doi: 10.3389/fcimb.2022.807668. PMID: 35186794; PMCID: PMC8851030.
22. De Socio G. V., Di Donato F., Paggi R., Gabrielli C., Belati A., Rizza G., et al.. (2018). Laboratory automation reduces time to report of positive blood cultures and improves management of patients with bloodstream infection. Eur. J. Clin. Microbiol. Infect. Dis. 37 (12), 2313–2322. doi: 10.1007/s10096-018-3377-5
23. Haleem A, Javaid M, Singh RP, Suman R. Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sens Int. 2021;2:100117. doi: 10.1016/j.sintl.2021.100117. Epub 2021 Jul 24. PMID: 34806053; PMCID: PMC8590973.
24. Kulengowski B., Ribes J. A., Burgess D. S. (2019). Polymyxin b etest® compared with gold-standard broth microdilution in carbapenem-resistant enterobacteriaceae exhibiting a wide ran-ge of polymyxin b MICs. Clin. Microbiol. Infect. 25 (1), 92–95. doi: 10.1016/j.cmi.2018.04.008
25. Camarlinghi G., Parisio E. M., Antonelli A., Nardone M., Coppi M., Giani T., et al.. (2019). Discrepancies in fosfomycin susceptibility testing of KPC-producing klebsiella pneu-moniae with various commercial methods. Diagn. Microbiol. Infect. Dis. 93 (1), 74–76. doi: 10.1016/j.diagmicrobio.2018.07.014
26. Simner P. J., Patel R. (2020). Cefiderocol antimicrobial susceptibility testing considerations: the achilles' heel of the Trojan horse? J. Clin. Microbiol. 17, 59(1):e00951–20. doi: 10.1128/JCM.00951-20
27. Antonelli A., Giani T., Di Pilato V., Riccobono E., Perriello G., Mencacci A., et al.. (2019). KPC-31 expressed in a ceftazidime/avibactam-resistant klebsiella pneumoniae is asso-ciated with relevant detection issues. J. Antimicrob. Chemother. 74 (8), 2464–2466. doi: 10.1093/jac/dkz156
28. Culbreath K., Piwonka H., Korver J., Noorbakhsh M. (2021). Benefits Derived From Full Labo-ratory Automation in Microbiology: A Tale of Four Laboratories. J. Clin. Microbiol. 59 (3), e01969–20. doi: 10.1128/JCM.01969-20.
29. Zimmermann S. (2021). Laboratory automation in the microbiology laboratory: an ongoing jo-urney, not a tale? J. Clin. Microbiol. 59 (3), e02592–e02520. doi: 10.1128/JCM.02592-20
30. Croxatto A., Prod'hom G., Faverjon F., Rochais Y., Greub G. (2016). Laboratory automation in clinical bacteriology: what system to choose? Clin. Microbiol. Infect. 22 (3), 217–235. doi: 10.1016/j.cmi.2015.09.030
31. Croxatto A., Marcelpoil R., Orny C., Morel D., Prod'hom G., Greub G. (2017). Towards auto-mated detection, semi-quantification and identification of microbial growth in clinical bacteri-ology: a proof of concept. BioMed. J. 40 (6), 317–328. doi: 10.1016/j.bj.2017.09.001
32. Burckhardt I., Last K., Zimmermann S. (2019). Shorter incubation times for detecting multi-drug resistant bacteria in patient samples: defining early imaging time points using growth ki-netics and total laboratory automation. Ann. Lab. Med. 39 (1), 43–49. doi: 10.3343/alm.2019.39.1.43
33. Faron M. L., Buchan B. W., Vismara C., Lacchini C., Bielli A., Gesu G., et al.. (2016. a). Automated scoring of chromogenic media for detection of methicillin-resistant staphylo-coccus aureus by use of WASPLab image analysis software. J. Clin. Microbiol. 54 (3), 620–624. doi: 10.1128/JCM.02778-15
34. Faron M. L., Buchan B. W., Coon C., Liebregts T., van Bree A., Jansz A. R., et al.. (2016. b). Automatic digital analysis of chromogenic media for vancomycin-Resistant-Enterococcus screens using copan WASPLab. J. Clin. Microbiol. 54 (10), 2464–2469. doi: 10.1128/JCM.01040-16
35. Foschi C., Gaibani P., Lombardo D., Re M. C., Ambretti S. (2020). Rectal screening for carba-penemase-producing enterobacteriaceae: a proposed workflow. J. Glob Antimicrob. Resist. 21, 86–90. doi: 10.1016/j.jgar.2019.10.012
36. Dauwalder O., Landrieve L., Laurent F., de Montclos M., Vandenesch F., Lina G. (2016). Does bacteriology laboratory automation reduce time to results and increase quality manage-ment? Clin. Microbiol. Infect. 22 (3), 236–243. doi: 10.1016/j.cmi.2015.10.037
37. Burns BL, Rhoads DD, Misra A. The Use of Machine Learning for Image Analysis Artificial Intelligence in Clinical Microbiology. J Clin Microbiol. 2023 Sep 21;61(9):e0233621. doi: 10.1128/jcm.02336-21. Epub 2023 Jul 3. PMID: 37395657; PMCID: PMC10575257.
38. Garcia-Vidal C, Sanjuan G, Puerta-Alcalde P, Moreno-García E, Soriano A. Artificial intelli-gence to support clinical decision-making processes. EBioMedicine. 2019 Aug;46:27-29. doi: 10.1016/j.ebiom.2019.07.019. Epub 2019 Jul 11. PMID: 31303500; PMCID: PMC6710912.
39. Sharma A, Tiwari S, Deb MK, Marty JL. Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2): a global pandemic and treatment strategies. Int J Antimicrob Agents. 2020 Aug;56(2):106054. doi: 10.1016/j.ijantimicag.2020.106054. Epub 2020 Jun 10. PMID: 32534188; PMCID: PMC7286265.
40. Takahashi H, Ichinose N, Okada Y. False-negative rate of SARS-CoV-2 RT-PCR tests and its relationship to test timing and illness severity: A case series. IDCases. 2022;28:e01496. doi: 10.1016/j.idcr.2022.e01496. Epub 2022 Apr 5. PMID: 35402162; PMCID: PMC8982474.
41. Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in the diagnosis of COVID-19: chal-lenges and perspectives. Int J Biol Sci. 2021 Apr 10;17(6):1581-1587. doi: 10.7150/ijbs.58855. PMID: 33907522; PMCID: PMC8071762.
42. Raje S, Reddy N, Jerbi H, Randhawa P, Tsaramirsis G, Shrivas NV, Pavlopoulou A, Stojmeno-vić M, Piromalis D. Applications of Healthcare Robots in Combating the COVID-19 Pandemic. Appl Bionics Biomech. 2021 Nov 23;2021:7099510. doi: 10.1155/2021/7099510. Retraction in: Appl Bionics Biomech. 2023 Nov 29;2023:9826597. PMID: 34840604; PMCID: PMC8611354.
43. Shen Y, Guo D, Long F, Mateos LA, Ding H, Xiu Z, Hellman RB, King A, Chen S, Zhang C, Tan H. Robots Under COVID-19 Pandemic: A Comprehensive Survey. IEEE Access. 2020 Dec 18;9:1590-1615. doi: 10.1109/ACCESS.2020.3045792. PMID: 34976569; PMCID: PMC8675561.
44. Chen Y, Wang Q, Chi C, Wang C, Gao Q, Zhang H, Li Z, Mu Z, Xu R, Sun Z, Qian H. A colla-borative robot for COVID-19 oropharyngeal swabbing. Rob Auton Syst. 2022 Feb;148:103917. doi: 10.1016/j.robot.2021.103917. Epub 2021 Oct 26. PMID: 34720413; PMCID: PMC8548047.
45. Ge MC, Kuo AJ, Liu KL, Wen YH, Chia JH, Chang PY, Lee MH, Wu TL, Chang SC, Lu JJ. Ro-utine identification of microorganisms by matrix-assisted laser desorption ionization time-of-flight mass spectrometry: Success rate, economic analysis, and clinical outcome. J Microbiol Immunol Infect. 2017 Oct;50(5):662-668. doi: 10.1016/j.jmii.2016.06.002. Epub 2016 Jun 24. PMID: 27426930.
46. Elbehiry A, Aldubaib M, Abalkhail A, Marzouk E, ALbeloushi A, Moussa I, Ibrahem M, Alba-zie H, Alqarni A, Anagreyyah S, Alghamdi S, Rawway M. How MALDI-TOF Mass Spectro-metry Technology Contributes to Microbial Infection Control in Healthcare Settings. Vaccines (Basel). 2022 Nov 8;10(11):1881. doi: 10.3390/vaccines10111881. PMID: 36366389; PMCID: PMC9699604.
47. Vrioni G, Tsiamis C, Oikonomidis G, Theodoridou K, Kapsimali V, Tsakris A. MALDI-TOF mass spectrometry technology for detecting biomarkers of antimicrobial resistance: current achievements and future perspectives. Ann Transl Med. 2018 Jun;6(12):240. doi: 10.21037/atm.2018.06.28. PMID: 30069442; PMCID: PMC6046294.
48. Barth PO, Roesch EW, Lutz L, de Souza ÂC, Goldani LZ, Pereira DC. Rapid bacterial identifi-cation by MALDI-TOF MS directly from blood cultures and rapid susceptibility testing: A simple approach to reduce the turnaround time of blood cultures. Braz J Infect Dis. 2023 Jan-Feb;27(1):102721. doi: 10.1016/j.bjid.2022.102721. Epub 2022 Nov 30. PMID: 36462577; PMCID: PMC9727634.
49. Zhang J, Yang F, Sun Z, Fang Y, Zhu H, Zhang D, Zeng X, Liu W, Liu T, Liu Y, Chi W, Wang S, Ding L, Wu Y, Zhang Y, Zhao H. Rapid and precise identification of bloodstream infections using a pre-treatment protocol combined with high-throughput multiplex genetic detection sys-tem. BMC Infect Dis. 2022 Nov 8;22(1):823. doi: 10.1186/s12879-022-07793-6. PMID: 36348318; PMCID: PMC9644494.
50. Bayot ML, Bragg BN. Antimicrobial Susceptibility Testing. 2022 Oct 10. In: StatPearls [Inter-net]. Treasure Island (FL): StatPearls Publishing; 2024 Jan–. PMID: 30969536.
51. Weis CV, Jutzeler CR, Borgwardt K. Machine learning for microbial identification and anti-microbial susceptibility testing on MALDI-TOF mass spectra: a systematic review. Clin Mic-robiol Infect. 2020 Oct;26(10):1310-1317. doi: 10.1016/j.cmi.2020.03.014. Epub 2020 Mar 23. PMID: 32217160.
52. Idelevich EA, Nix ID, Busch JA, Sparbier K, Drews O, Kostrzewa M, Becker K. Rapid Simulta-neous Testing of Multiple Antibiotics by the MALDI-TOF MS Direct-on-Target Microdroplet Growth Assay. Diagnostics (Basel). 2021 Sep 29;11(10):1803. doi: 10.3390/diagnostics11101803. PMID: 34679499; PMCID: PMC8534412.
53. Durack J, Lynch SV. The gut microbiome: Relationships with disease and opportunities for the-rapy. J Exp Med. 2019 Jan 7;216(1):20-40. doi: 10.1084/jem.20180448. Epub 2018 Oct 15. PMID: 30322864; PMCID: PMC6314516.
54. Pérez-Cobas AE, Gomez-Valero L, Buchrieser C. Metagenomic approaches in microbial eco-logy: an update on whole-genome and marker gene sequencing analyses. Microb Genom. 2020 Aug;6(8):mgen000409. doi: 10.1099/mgen.0.000409. Epub 2020 Jul 24. PMID: 32706331; PMCID: PMC7641418.
55. Wensel CR, Pluznick JL, Salzberg SL, Sears CL. Next-generation sequencing: insights to ad-vance clinical investigations of the microbiome. J Clin Invest. 2022 Apr 1;132(7):e154944. doi: 10.1172/JCI154944. PMID: 35362479; PMCID: PMC8970668.
56. Marcos-Zambrano LJ, Karaduzovic-Hadziabdic K, Loncar Turukalo T, Przymus P, Trajkovik V, Aasmets O, Berland M, Gruca A, Hasic J, Hron K, Klammsteiner T, Kolev M, Lahti L, Lo-pes MB, Moreno V, Naskinova I, Org E, Paciência I, Papoutsoglou G, Shigdel R, Stres B, Vilne B, Yousef M, Zdravevski E, Tsamardinos I, Carrillo de Santa Pau E, Claesson MJ, Moreno-Indias I, Truu J. Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment. Front Mic-robiol. 2021 Feb 19;12:634511. doi: 10.3389/fmicb.2021.634511. PMID: 33737920; PMCID: PMC7962872.
57. Sharon I, Quijada NM, Pasolli E, Fabbrini M, Vitali F, Agamennone V, Dötsch A, Selberherr E, Grau JH, Meixner M, Liere K, Ercolini D, de Filippo C, Caderni G, Brigidi P, Turroni S. The Core Human Microbiome: Does It Exist and How Can We Find It? A Critical Review of the Concept. Nutrients. 2022 Jul 13;14(14):2872. doi: 10.3390/nu14142872. PMID: 35889831; PMCID: PMC9323970.
58. Rupp N, Ries R, Wienbruch R, Zuchner T. Can I benefit from laboratory automation? A deci-sion aid for the successful introduction of laboratory automation. Anal Bioanal Chem. 2024 Jan;416(1):5-19. doi: 10.1007/s00216-023-05038-2. Epub 2023 Nov 30. PMID: 38030885; PMCID: PMC10758358.
59. Rhoads DD, Sintchenko V, Rauch CA, Pantanowitz L. Clinical microbiology informatics. Clin Microbiol Rev. 2014 Oct;27(4):1025-47. doi: 10.1128/CMR.00049-14. PMID: 25278581; PMCID: PMC4187636.
60. Ford BA, McElvania E. Machine Learning Takes Laboratory Automation to the Next Level. J Clin Microbiol. 2020 Mar 25;58(4):e00012-20. doi: 10.1128/JCM.00012-20. PMID: 32024725; PMCID: PMC7098768.
61. Buchan BW, Ledeboer NA. Emerging technologies for the clinical microbiology laboratory. Clin Microbiol Rev. 2014 Oct;27(4):783-822. doi: 10.1128/CMR.00003-14. PMID: 25278575; PMCID: PMC4187641.
62. Tjandra KC, Ram-Mohan N, Abe R, Hashemi MM, Lee JH, Chin SM, Roshardt MA, Liao JC, Wong PK, Yang S. Diagnosis of Bloodstream Infections: An Evolution of Technologies towards Accurate and Rapid Identification and Antibiotic Susceptibility Testing. Antibiotics (Basel). 2022 Apr 12;11(4):511. doi: 10.3390/antibiotics11040511. PMID: 35453262; PMCID: PMC9029869.
63. Krupanandan RK, Kapalavai SK, Ekka AS, Balusamy I, Sadasivam K, Nambi P S, Ramachand-ran B. Active surveillance for carbapenem resistant enterobacteriaceae (CRE) using stool cultu-res as a method to decrease CRE infections in the pediatric intensive care unit (PICU). Indian J Med Microbiol. 2023 Jul-Aug;44:100370. doi: 10.1016/j.ijmmb.2023.100370. Epub 2023 May 2. PMID: 37356850.
64. Çalık Ş, Kansak N, Aksaray S. Phenotypic detection of carbapenemase production in carbape-nem-resistant isolates with the rapid carbapenemase detection method (rCDM). J Microbiol Methods. 2022 Sep;200:106536. doi: 10.1016/j.mimet.2022.106536. Epub 2022 Jul 2. PMID: 35792236.