Role of Artificial Intelligence in Bacterial Diagnosis
Abstract
Diagnosis is an important aspect in healthcare and is important in the identification of microorganisms responsible for infections and disorders. In the bacterial diagnostics field, artificial intelligence (AI) systems can be utilised to improve illness management, medication discovery, prediction of antibiotic resistance and epidemiological surveillance. Artificial intelligence systems may detect infections fast and accurately. Also, the development of AI programs for bacterial testing is important to reduce diagnostic errors and thus to provide adequate therapy. This paper intends to explore the function of AI in diagnosing harmful bacteria and to highlight the most reliable AI programs available. The report also discusses the major problems we have in applying AI to the diagnosis of harmful bacteria. AI has brought in the high motive in the bacterial prognosis by providing better instruments that surpass several barriers of the conventional tactics. AI-based totally approaches, especially in automated photo evaluation, colony counting, genome sequencing, phylogenetic evaluation, and immunological diagnostics, have substantially advanced the rate, accuracy, and reliability of pathogen identification. “These technologies minimise the need for guide interpretation and allow for more standardised, efficient lab workflows”. In the future, the ongoing improvement and integration of AI technology are anticipated to convert bacterial diagnostics into a greater fast, precise, and on hand procedure, strengthening healthcare structures and advancing global public health.
Keywords:
Artificial Intelligence, Bacterial infections, Diagnosis, Deep learning, Machine learning
References
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4. Zeng Y, Wang C, Ye Q, Liu G, Zhang L, Wan J, Zhu Y. Machine learning model of imipenem-resistant Klebsiella pneumoniae based on MALDI-TOF-MS platform: An observational study. Health Sci Rep. 2023 Apr 14;6(9):e1108.
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6. Wang J, Xia C, Wu Y, Tian X, Zhang K, Wang Z. Rapid Detection of Carbapenem-Resistant Klebsiella pneumoniae Using Machine Learning and MALDI-TOF MS Platform. Infect Drug Resist. 2022 Jul 12;15:3703-3710.
7. Weis CV, Jutzeler CR, Borgwardt K. Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review. Clin Microbiol Infect. 2020 Oct;26(10):1310-1317.
8. Gato E, Arroyo MJ, Méndez G, Candela A, Rodiño-Janeiro BK, Fernández J, Rodríguez-Sánchez B, Mancera L, Arca-Suárez J, Beceiro A, Bou G, Oviaño M. Direct Detection of Carbapenemase-Producing Klebsiella pneumoniae by MALDI-TOF Analysis of Full Spectra Applying Machine Learning. J Clin Microbiol. 2023 Jun 20;61(6):e0175122.
9. Bristy SF, Md Reaz Uddin MR, Jahan N, Islam DZ, Meghla BA, Tisha TA, Uddin MA, Islam MT, Mie M, Kobatake E, Khondoker MU, Mou TJ, Haq MA, Islam MF, Islam Z, Hossain MS, Azmuda N, Jamiruddin MR, Sinha S, Haque M, Adnan N. Misdiagnosis of Bacterial Pathogens by the Diagnostic Centers: A Potential Route for Antibiotic Resistance. J Appl Pharm Sci. 2024;14(06):163–173.
10. Eldin C, Parola P, Raoult D. Limitations of diagnostic tests for bacterial infections. Med Mal Infect. 2019 Mar;49(2):98-101.
11. Kazaal MA, Hamad WA, Atiya WH, Saeed BJ, Abd-Alsatar AN. Impact of Antibiotic Resistance on Sustainable Development Goals.AIP Conference Proceedings 2776, 020016 (2023); https://doi.org/10.1063/5.0137246 .
12. Eldin C, Jaulhac B, Mediannikov O, Arzouni JP, Raoult D. Values of diagnostic tests for the various species of spirochetes. Med Mal Infect. 2019 Mar;49(2):102-111.
13. Marín M, Cercenado E, Sánchez-Carrillo C, Ruiz A, Gómez González Á, Rodríguez-Sánchez B, Bouza E. Accurate Differentiation of Streptococcus pneumoniae from other Species within the Streptococcus mitis Group by Peak Analysis Using MALDI-TOF MS. Front Microbiol. 2017 Apr 25;8:698.
14. Giraud-Gatineau A, Texier G, Garnotel E, Raoult D, Chaudet H. Insights Into Subspecies Discrimination Potentiality From Bacteria MALDI-TOF Mass Spectra by Using Data Mining and Diversity Studies. Front Microbiol. 2020 Aug 13;11:1931.
15. National Academies of Sciences, Engineering and Medicine. 2015. Improving Diagnosis in Health Care. Washington, DC: National Academies Press. 10.17226/21794. [PubMed] [CrossRef]
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17. Zwaan L, de Bruijne M, Wagner C, et al. Patient record review of the incidence, consequences, and causes of diagnostic adverse events. Arch Intern Med. 2010;170(12):1015–21.
18. Agarwal R. Quality-improvement measures as effective ways of preventing laboratory errors. Lab Med. 2014;45(2):e80-e88. doi:10.1309/LMD0YIFPTOWZONAD
19. Lokonon BE, Houetohossou A, Tchede B, et al. The use of Artificial Intelligence based modelling techniques in One Health-related infectious disease studies in Sub-Saharan Africa: a review. Front ArtifIntell. 2026;9:1778800.
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21. Hudu SA, Alshrari AS, Abu-Shoura EJI, Osman A, Jimoh AO. A Critical Review of the Prospect of Integrating Artificial Intelligence in Infectious Disease Diagnosis and Prognosis. InterdiscipPerspect Infect Dis. 2025;2025:6816002. Published 2025 Mar 6.
22. Behara K, Bhero E, Agee JT, Gonela V. Artificial intelligence in medical diagnostics: a review from a South African context. Sci Afr. 2022;17:e01360. doi:10.1016/j.sciaf.2022.e01360
23. Ferrari A, Lombardi S, Signoroni A. Bacterial colony counting by convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. 2015:7458-7461.
24. Chiang PJ, Tseng MJ, He ZS, Li CH. Automated counting of bacterial colonies by image analysis. J Microbiol Methods. 2015;108:74-82.
25. Cao L, Zeng L, Wang Y, et al. U²-Net and ResNet50-based automatic pipeline for bacterial colony counting. Microorganisms. 2024;12(1):201.
26. Zhang B, Zhou Z, Cao W, et al. A new few-shot learning method for bacterial colony counting based on edge computing. Biology. 2022;11(2):156.
27. Pintavirooj C, Bunkum M, Vongmanee N, Nampeng J, Visitsattapongse S. Bacterial colony counting and classification system based on deep learning model. Appl Sci. 2026;16(3):1313.
28. Zhang L. Machine learning for enumeration of cell colony forming units. Vis Comput Ind Biomed Art. 2022;5:26.
29. Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16(6):321–332.
30. Arango-Argoty G, Garner E, Pruden A, Heath LS, Vikesland P, Zhang L. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome. 2018;6(1):23.
31. Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A. A primer on deep learning in genomics. Nat Genet. 2019;51(1):12–18.
32. Segata N, Börnigen D, Morgan XC, Huttenhower C. PhyloPhlAn: improved phylogenetic and taxonomic placement of microbes. Nat Commun. 2013;4:2304.
33. Jain C, Rodriguez RL, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9(1):5114.
34. Lees JA, Vehkala M, Välimäki N, et al. Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes. Nat Commun. 2016;7:12797.
35. Didelot X, Bowden R, Wilson DJ, Peto TEA, Crook DW. Transforming clinical microbiology with bacterial genome sequencing. Nat Rev Genet. 2012;13(9):601–612.
36. Su M, Satola SW, Read TD. Genome-based prediction of bacterial antibiotic resistance. J Clin Microbiol. 2019;57(3):e01405-18.
37. Benson DA, Cavanaugh M, Clark K, et al. GenBank. Nucleic Acids Res. 2018;46(D1):D41–D47.
38. Leinonen R, Sugawara H, Shumway M. The Sequence Read Archive. Nucleic Acids Res. 2011;39(Database issue):D19–D21.
39. Kim JI, Maguire F, Tsang KK, Gouliouris T, Peacock SJ, McAllister TA, et al. Machine learning for antimicrobial resistance prediction: current practice, limitations, and clinical perspective. Clin Microbiol Rev. 2022;35(3):e00179-21. doi:10.1128/cmr.00179-21
40. Moradigaravand D, Palm M, Farewell A, Mustonen V. Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data. PLoSComput Biol. 2018;14(12):e1006258.
41. Quainoo S, Coolen JPM, van Hijum SAFT, et al. Whole-genome sequencing of bacterial pathogens: the future of nosocomial outbreak analysis. Clin Microbiol Rev. 2017;30(4):1015–1063.
42. Nguyen M, Long SW, McDermott PF, et al. Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella. J Clin Microbiol. 2019;57(2):e01260-18.
43. Chen ML, Doddi A, Royer J, Freschi L, Schito M, Ezewudo M, et al. Beyond multidrug resistance: leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction. EBioMedicine. 2019;43:356-369. doi:10.1016/j.ebiom.2019.04.016
44. Yang Y, Niehaus KE, Walker TM, Iqbal Z, Walker AS, Wilson DJ. Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data. Bioinformatics. 2018;34(10):1666–1671.
45. Phelan JE, O’Sullivan DM, Machado D, et al. Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs. Genome Med. 2019;11(1):41.
46. Smith KP, Kang AD, Kirby JE. Automated interpretation of blood culture Gram stains by use of a deep convolutional neural network. J Clin Microbiol. 2018;56(3):e01521-17. doi:10.1128/JCM.01521-17
47. Moen E, Bannon D, Kudo T, et al. Deep learning for cellular image analysis. Nat Methods. 2019;16(12):1233-1246.
48. Ferrari A, Lombardi S, Signoroni A. Bacterial colony counting with convolutional neural networks in digital microbiology imaging. Pattern Recognit. 2017;61:629-640.
49. Zieliński B, Plichta A, Misztal K, et al. Deep learning approach to bacterial colony classification. PLoS One. 2017;12(9):e0184554.
50. Ching T, Himmelstein DS, Beaulieu-Jones BK, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15(141):20170387.
51. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
52. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317-1318.
53. Holzinger A, Langs G, Denk H, et al. Causability and explainability of AI in medicine. Wiley Interdiscip Rev Data Min KnowlDiscov. 2019;9(4):e1312.
54. Angermueller C, Pärnamaa T, Parts L, et al. Deep learning for computational biology. Mol Syst Biol. 2016;12(7):878.
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10/09/2026
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Abdullah, L. (2026). Role of Artificial Intelligence in Bacterial Diagnosis. The Journal of Multidisciplinary Research, 6(2), 47-59. https://doi.org/10.37022/tjmdr.v6i2.957
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