ARTIFICIAL INTELLIGENCE-BASED TOOLS APPLIED TO PATHOLOGICAL DIAGNOSIS OF MICROBIOLOGICAL DISEASES
Main Article Content
Keywords
Artificial Intelligence, Machine Learning, Deep Learning, Microbiological Diseases, Digital Pathology, Clinical Microbiology, Molecular Diagnostics
Abstract
Pathological testing is an important part of the world diagnostic picture of infectious complications, which forms the necessary evidence in making clinical decisions in various healthcare facilities. Traditional microbiological methods, including microscopy, culture and molecular assays, are the practice standard, however, these modalities are often labour-intensive, time-consuming and depend on expert knowledge, which adds to extended diagnostic delays as well as systemic inefficiencies. Artificial intelligence in recent years has become a disruptive technology in laboratory automation through the implementation of Pathology-Digital Systems, which are driven by Digital Image Analysis. These tools are designed to support clinical practice by helping to identify pathogens using modern digital pathology and identifying the microbe using machine learning algorithms and interpreting the complex datasets of the molecules. Convolutional neural networks have already been shown to perform at the state-of-the-art level when used as diagnostic instruments to assess Gram-stained smears, peripheral blood preparations and histological sections and identify a large range of microorganisms- bacterial, viral, fungal and parasitic agents with high precision. AI and advanced forms of diagnostics like polymerase chain reaction (PCR), next-generation sequencing (NGS), and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) have contributed ample to the detection of pathogens, anticipation of antimicrobial resistance, and outbreak surveillance. However, issues related to data standardisation, model interpretability, and regulatory implementation during the implementation of AI in diagnostic platforms remain, but AI promises to enhance the effectiveness, accuracy, and clinical decision-making skills of microbiologists and pathologists in the laboratory diagnosis of infectious diseases. This review presents the current advances, real-world applications, current constraints, and future trends of AI-based diagnostic devices in the pathological microbiology domain.
References
2. Schubert, A.M.; Kostrzynska, M. Advances in the molecular-based techniques for the detection of bacterial pathogens. J. Microbiol. Methods 2001, 47, 235–252.
3. Lamb, L.E.; Bartolone, S.N.; Ward, E.; Chancellor, M.B. Rapid detection of novel coronavirus (COVID-19) by reverse transcription-loop-mediated isothermal amplification. PLoS ONE 2020, 15, e0234682.
4. Mullis, K.; Faloona, F.; Scharf, S.; Saiki, R.; Horn, G.; Erlich, H. Specific enzymatic amplification of DNA in vitro: The polymerase chain reaction. Cold Spring Harb. Symp. Quant. Biol. 1986, 51, 263–273.
5. Corman, V.M.; Landt, O.; Kaiser, M.; Molenkamp, R.; Meijer, A.; Chu, D.K.; Bleicker, T.; Brünink, S.; Schneider, J.; Schmidt, M.L.; et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro Surveill. 2020, 25, 2000045.
6. Dramé, M.; Tabue Teguo, M.; Proye, E.; Hequet, F.; Hentzien, M.; Kanagaratnam, L.; Godaert, L. Should RT-PCR be considered a gold standard in the diagnosis of COVID-19? J. Med. Virol. 2020, 92, 2312–2313.
7. Metzker, M.L. Sequencing technologies—The next generation. Nat. Rev. Genet. 2010, 11, 31–46.
8. Canto´ n R, Ako´ va M, Carmeli Y, Giske CG, Glupczynski Y, Gniadkowski M, Livermore DM, Miriagou V, Naas T, Rossolini GM et al.: Rapid evolution and spread of carbapenemases among Enterobacteriaceae in Europe. Clin Microbiol Infect 2012, 18:413-431.
9. GBD 2017 Causes of Death Collaborators: Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018, 392:1736-1788.
10. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM et al.: The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016, 315:801-810.
11. Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, Colombara DV, Ikuta KS, Kissoon N, Finfer S et al.: Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet 2020, 395:200-211.
12. Wang, P.X.; Sun, Y.; Li, X.; Wang, L.; Xu, Y.; He, L.L.; Li, G.L. Recent advances in dual recognition based surface enhanced Raman scattering for pathogenic bacteria detection: A review. Anal. Chim. Acta 2021, 1157, 338279.
13. Andryukov, B.G.; Lyapun, I.N.; Matosova, E.V.; Somova, L.M. Biosensor technologies in medicine: From detection of biochemical markers to research into molecular targets (Review). Mod. Technol. Med. 2020, 12, 71–85.
14. Zheng, L.; Qi, P.; Zhang, D. A simple, rapid and cost-effective colorimetric assay based on the 4-mercaptophenylboronic acid functionalized silver nanoparticles for bacteria monitoring. Sens. Actuators B Chem 2018, 260, 983–989.
15. Pearcy N, Hu Y, Baker M, Maciel-Guerra A, Xue N, Wang W, Kaler J, Peng Z, Li F, Dottorini T. 2021. Genome-scale metabolic models and machine learning reveal genetic determinants of antibiotic resistance in Escherichia coli and unravel the underlying metabolic adaptation mechanisms. mSystems 6:e0091320. doi: 10.1128/mSystems.00913-20.
16. Nguyen M, Long SW, McDermott PF, Olsen RJ, Olson R, Stevens RL, Tyson GH, Zhao S, Davis JJ. 2019. Using machine learning to predict antimicrobial MICs and asociated genomic features for nontyphoidal Salmonella. J Clin Microbiol 57:e01260-18. doi: 10.1128/JCM.01260-18.
17. Humphries RM, Bragin E, Parkhill J, Morales G, Schmitz JE, Rhodes PA. 2023. Machine-learning model for prediction of cefepime susceptibility in Escherichia coli from whole-genome sequencing data. J Clin Microbiol 61. doi: 10.1128/jcm.01431-22.
18. Smith KP, Wang H, Durant TJ, Mathison BA, Sharp SE, Kirby JE, Long SW, Rhoads DD. 2020. Applications of artificial intelligence in clinical microbiology diagnostic testing. Clinical Microbiology Newsletter 42:61–70. doi: 10.1016/j.clinmicnews.2020.03.006.
19. Cheng, J.Y.; Abel, J.T.; Balis, U.G.; McClintock, D.S.; Pantanowitz, L. Challenges in the development, deployment, and regulation of artificial intelligence in anatomic pathology. Am. J. Pathol. 2021, 191, 1684–1692.
20. Bagabir, S.A.; Ibrahim, N.K.; Bagabir, H.A.; Ateeq, R.H. Covid-19 and Artificial Intelligence: Genome sequencing, drug development and vaccine discovery. J. Infect. Public Health 2022, 15, 289–296.
21. Chiu, H.-Y.R.; Hwang, C.-K.; Chen, S.-Y.; Shih, F.-Y.; Han, H.-C.; King, C.-C.; Gilbert, J.R.; Fang, C.-C.; Oyang, Y.-J. Machine learning for emerging infectious disease field responses. Sci. Rep. 2022, 12, 328.
22. Pham, T.-H.; Qiu, Y.; Zeng, J.; Xie, L.; Zhang, P. A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing. Nat. Mach. Intell. 2021, 3, 247–257.
23. Ong, E.; Wong, M.U.; Huffman, A.; He, Y. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. Front. Immunol. 2020, 11, 1581.
24. Ong, E.; Cooke, M.F.; Huffman, A.; Xiang, Z.; Wong, M.U.; Wang, H.; Seetharaman, M.; Valdez, N.; He, Y. Vaxign2: The second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning. Nucleic Acids Res. 2021, 49, W671–W678.
25. Dutta, D.; Naiyer, S.; Mansuri, S.; Soni, N.; Singh, V.; Bhat, K.H. COVID-19 diagnosis: A comprehensive review of the RT-qPCR method for detection of SARS-CoV-2. Diagnostics 2022, 12, 1503.
26. Fournier, P.-E.; Drancourt, M.; Colson, P.; Rolain, J.-M. Modern clinical microbiology: New challenges and solutions. Nat. Rev. Microbiol. 2013, 11, 574–585.
27. Uddin, T.M.; Chakraborty, A.J.; Khusro, A.; Zidan, B.R.M.; Mitra, S.; Bin Emran, T.; Dhama, K.; Ripon, K.H.; Gajdács, M.; Sahibzada, M.U.K.; et al. Antibiotic resistance in microbes: History, mechanisms, therapeutic strategies and future prospects. J. Infect. Public Health 2021, 14, 1750–1766.
28. Singer, M.; Deutschman, C.S.; Seymour, C.W.; Shankar-Hari, M.; Annane, D.; Bauer, M.; Bellomo, R.; Bernard, G.R.; Chiche, J.-D.; Coopersmith, C.M.; et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016, 315, 801–810.
29. Smith, K.P.; Wang, H.; Durant, T.J.; Mathison, B.A.; Sharp, S.E.; Kirby, J.E.; Long, S.W.; Rhoads, D.D. Applications of artificial intelligence in clinical microbiology diagnostic testing. Clin. Microbiol. Newsl. 2020, 42, 61–70.
30. Nayak, D.S.K.; Mahapatra, S.; Routray, S.P.; Sahoo, S.; Sahoo, S.K.; Fouda, M.M.; Singh, N.; Isenovic, E.R.; Saba, L.; Suri, J.S.; et al. aiGeneR 1.0: An Artificial Intelligence Technique for the Revelation of Informative and Antibiotic Resistant Genes in Escherichia coli. Front. Biosci.-Landmark 2024, 29, 82.
31. Samantray, D.; Tanwar, A.S.; Murali, T.S.; Brand, A.; Satyamoorthy, K.; Paul, B. A Comprehensive Bioinformatics Resource Guide for Genome-Based Antimicrobial Resistance Studies. OMICS A J. Integr. Biol. 2023, 27, 445–460.
32. 148.Kumar Y., Koul A., Singla R., Ijaz M.F. Artificial intelligence in disease diagnosis: A systematic literature review, synthesizing framework and future research agenda. J. Ambient. Intell. Humaniz. Comput. 2023;14:8459–8486. doi: 10.1007/s12652-021-03612-z.
33. 149.Mirbabaie M., Stieglitz S., Frick N.R.J. Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction. Health Technol. 2021;11:693–731. doi: 10.1007/s12553-021-00555-5.
34. 150.Arora G., Joshi J., Mandal R.S., Shrivastava N., Virmani R., Sethi T. Artificial intelligence in surveillance, diagnosis, drug discovery and vaccine development against COVID-19. Pathogens. 2021;10:1048. doi: 10.3390/pathogens10081048.
35. 151.Gerke S., Minssen T., Cohen G. Artificial Intelligence in Healthcare. Academic Press; Cambridge, MA, USA: 2020. Ethical and legal challenges of artificial intelligence-driven healthcare; pp. 295–336.
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
Inkashaf Alam
Department of Medical Laboratory Technology, University Institute of Allied Health Sciences, Chandigarh University, Mohali-140413, Punjab, India.
Deepika Kapoor
Department of Medical Laboratory Technology, University Institute of Allied Health Sciences, Chandigarh University, Mohali-140413, Punjab, India.
Sakshi
Department of Medical Laboratory Sciences, USAHS, Rayat Bahra University, Mohali, India.
Sai Charan Labishetty
Department of Optometry, University Institute of Allied Health Sciences, Chandigarh University, Mohali-140413, Punjab, India.
Zarque Ishan
School of Chemistry and Biochemistry Thapar University Patiala Punjab,147004, India.
Sadique Saqulain
Department of Pharmaceutical Sciences, Madhyanchal Professional University (MPU), Bhopal, Madhya Pradesh, India.
Hridayansh Bhardwaj
Department of Psychology University Institute of Liberal Arts and Humanities
