MicroBio Pharmaceuticals and Pharmacology | Online ISSN 2209-2161
REVIEWS   (Open Access)

Artificial intelligence for Improved Diagnosis and Treatment of Bacterial Infections

Md Habibur Rahman1, Syeda Anjuman Ara Aunni2, Bulbul Ahmed2, Md Moshiur Rahman3, Md Mahedi Hasan Shabuj2, Debashis Chandra Das4, Mst. Shahana Akter5, Abdullah Al Numan6

+ Author Affiliations

Microbial Bioactives 7(1) 1-18 https://doi.org/10.25163/microbbioacts.7110036

Submitted: 04 June 2024  Revised: 14 August 2024  Published: 15 August 2024 

5815.jpg?638706789114175449

Abstract

Background: Artificial intelligence (AI) is assuming a progressively crucial role in healthcare, providing enhanced diagnostic precision, tailored treatment strategies, and superior patient outcomes. Through the analysis of extensive medical data, including genetic information, lifestyle choices, and medical histories, AI has become an influential instrument in personalized medicine, especially for cancer and infectious diseases.  Methods: Oncology AI models evaluate genetic profiles and treatment histories to propose personalized chemotherapy protocols that minimize adverse effects while improving therapeutic efficacy.  When treating infectious diseases, tools like CombiANT use automated image analysis to check how well antibiotics work together. Portable antimicrobial susceptibility testing methods quickly find bacterial infections and make treatment plans that work best for them. Advanced AI systems, like ChatGPT-3, deliver precise differential diagnoses, accelerating clinical decision-making. Results: AI-driven personal therapy strategies have demonstrated considerable potential in cancer by enhancing therapeutic efficacy through the assessment of individual genetic variants. In infectious illnesses, AI's capacity to evaluate bacterial susceptibility and anticipate therapeutic responses is transforming treatment accuracy. Furthermore, AI models have attained significant diagnostic precision, highlighting their capacity to enhance and expedite clinical methodologies. Conclusion: Although AI has significant potential to revolutionize personalized healthcare, several hurdles remain.  This encompasses data privacy issues, the opaque nature of AI decision-making, and the sluggish progression of converting research into practical applications. Overcoming these challenges through cooperation, innovation, and comprehensive policy development is crucial for maximizing AI's potential to enhance personalized medicine and treatment outcomes.

Keywords: AI, personalized healthcare, diagnosis accuracy, treatment precision, bacterial infections

References

Abbasi, B. A., Saraf, D., Sharma, T., Sinha, R., Singh, S., Sood, S., et al. (2022). Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches. PeerJ, 10, e13380. https://doi.org/10.7717/peerj.13380

Abu-Aqil, G., Lapidot, I., Salman, A., & Huleihel, M. (2023). Quick detection of Proteus and Pseudomonas in patients’ urine and assessing their antibiotic susceptibility using infrared spectroscopy and machine learning. Sensors (Basel), 23, 8132. https://doi.org/10.3390/s23198132

Alami, H., Lehoux, P., Denis, J.-L., Motulsky, A., Petitgand, C., Savoldelli, M., et al. (2020). Organizational readiness for artificial intelligence in health care: Insights for decision-making and practice. Journal of Health Organization and Management, 35, 106–114. https://doi.org/10.1108/JHOM-03-2020-0074

Antimicrobial Resistance Collaborators. (2022). Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis. The Lancet, 399, 629–655. https://doi.org/10.1016/S0140-6736(21)02724-0

Baker, J., Timm, K., Faron, M., Ledeboer, N., & Culbreath, K. (2020). Digital image analysis for the detection of group B Streptococcus from ChromID Strepto B medium using PhenoMatrix algorithms. Journal of Clinical Microbiology, 59, e01902–e01919. https://doi.org/10.1128/JCM.01902-19

Baowaly, M. K., Lin, C.-C., Liu, C.-L., & Chen, K.-T. (2019). Synthesizing electronic health records using improved generative adversarial networks. Journal of the American Medical Informatics Association, 26, 228–241. https://doi.org/10.1093/jamia/ocy142

Baron, E. J. (2019). Clinical microbiology in underresourced settings. Clinical Laboratory Medicine, 39, 359–369. https://doi.org/10.1016/j.cll.2019.05.001

Beam, A. L., Motsinger-Reif, A., & Doyle, J. (2014). Bayesian neural networks for detecting epistasis in genetic association studies. BMC Bioinformatics, 15, 368. https://doi.org/10.1186/s12859-014-0368-0

Bilgin, G. B., Bilgin, C., Burkett, B. J., Orme, J. J., Childs, D. S., Thorpe, M. P., et al. (2024). Theranostics and artificial intelligence: New frontiers in personalized medicine. Theranostics, 14, 2367–2378. https://doi.org/10.7150/thno.94788

Burkovski, A. (2022). Host–pathogen interaction 3.0. International Journal of Molecular Sciences, 23, 12811. https://doi.org/10.3390/ijms232112811

Cath, C. (2018). Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376, 20180080. https://doi.org/10.1098/rsta.2018.0080

Cherkaoui, A., Renzi, G., Charretier, Y., Blanc, D. S., Vuilleumier, N., & Schrenzel, J. (2019). Automated incubation and digital image analysis of chromogenic media using Copan WASPLab enables rapid detection of vancomycin-resistant Enterococcus. Frontiers in Cellular and Infection Microbiology, 9, 379. https://doi.org/10.3389/fcimb.2019.00379

Ciccolini, M., Donker, T., Grundmann, H., Bonten, M. J. M., & Woolhouse, M. E. J. (2014). Efficient surveillance for healthcare-associated infections spreading between hospitals. Proceedings of the National Academy of Sciences of the United States of America, 111, 2271–2276. https://doi.org/10.1073/pnas.1308062111

Cisek, A. A., Dabrowska, I., Gregorczyk, K. P., & Wyzewski, Z. (2017). Phage therapy in bacterial infections treatment: One hundred years after the discovery of bacteriophages. Current Microbiology, 74, 277–283. https://doi.org/10.1007/s00284-016-1166-x

CLSI. (2023). Performance standards for antimicrobial susceptibility testing (33rd ed., CLSI supplement M100). Clinical and Laboratory Standards Institute. https://iacld.com/UpFiles/Documents/672a1c7c-d4ad-404e-b10e-97c19e21cdce.pdf [Accessed April 7, 2024].

d’Humières, C., Salmona, M., Dellière, S., Leo, S., Rodriguez, C., Angebault, C., et al. (2021). The potential role of clinical metagenomics in infectious diseases: Therapeutic perspectives. Drugs, 81(12), 1453–1466. https://doi.org/10.1007/s40265-021-01572-4

Dande, P., & Samant, P. (2018). Acquaintance to artificial neural networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review. Tuberculosis (Edinburgh), 108, 1–9. https://doi.org/10.1016/j.tube.2017.09.006

Deelder, W., Napier, G., Campino, S., Palla, L., Phelan, J., & Clark, T. G. (2022). A modified decision tree approach to improve the prediction and mutation discovery for drug resistance in Mycobacterium tuberculosis. BMC Genomics, 23(1), 46. https://doi.org/10.1186/s12864-022-08291-4

Deneke, C., Rentzsch, R., & Renard, B. Y. (2017). PaPrBaG: A machine learning approach for the detection of novel pathogens from NGS data. Scientific Reports, 7, 39194. https://doi.org/10.1038/srep39194

Desruisseaux, C., Broderick, C., Lavergne, V., Sy, K., Garcia, D.-J., Barot, G., et al. (2024). Retrospective validation of MetaSystems’ deep-learning-based digital microscopy platform with assistance compared to manual fluorescence microscopy for detection of mycobacteria. Journal of Clinical Microbiology, 62(1), e0106923. https://doi.org/10.1128/jcm.01069-23

Deusenbery, C., Wang, Y., & Shukla, A. (2021). Recent innovations in bacterial infection detection and treatment. ACS Infectious Diseases, 7(3), 695–720. https://doi.org/10.1021/acsinfecdis.0c00890

Dillard, L. R., Glass, E. M., Lewis, A. L., Thomas-White, K., & Papin, J. A. (2023). Metabolic network models of the Gardnerella pangenome identify key interactions with the vaginal environment. mSystems, 8(1), e0068922. https://doi.org/10.1128/msystems.00689-22

Dou, X., Yang, F., Wang, N., Xue, Y., Hu, H., & Li, B. (2023). Rapid detection and analysis of Raman spectra of bacteria in multiple fields of view based on image stitching technique. Frontiers in Bioscience-Landmark, 28(1), 249. https://doi.org/10.31083/j.bl2810249

Durmus Tekir, S., Çakir, T., Ardiç, E., Sayilirbas, A. S., Konuk, G., Konuk, M., et al. (2013). PHISTO: Pathogen–host interaction search tool. Bioinformatics, 29(11), 1357–1358. https://doi.org/10.1093/bioinformatics/btt137

Ekins, S., Godbole, A. A., Kéri, G., Orfi, L., Pato, J., Bhat, R. S., et al. (2017). Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I. Tuberculosis (Edinburgh), 103, 52–60. https://doi.org/10.1016/j.tube.2017.01.005

Eldin, C., Parola, P., & Raoult, D. (2019). Limitations of diagnostic tests for bacterial infections. Médecine et Maladies Infectieuses, 49(2), 98–101. https://doi.org/10.1016/j.medmal.2018.12.004

Elemento, O. (2024). How artificial intelligence unravels the complex web of cancer drug response. Cancer Research, 84(15), 1745–1746. https://doi.org/10.1158/0008-5472.CAN-24-1123

Ernst, D., Bolton, G., Recktenwald, D., Cameron, M. J., Danesh, A., Persad, D., et al. (2006). Bead-based flow cytometric assays: A multiplex assay platform with applications in diagnostic microbiology. In Advanced Techniques in Diagnostic Microbiology (pp. 427–443). Springer US.

Evans, L., Rhodes, A., Alhazzani, W., Antonelli, M., Coopersmith, C. M., French, C., et al. (2021). Surviving sepsis campaign: International guidelines for management of sepsis and septic shock 2021. Intensive Care Medicine, 47(11), 1181–1247. https://doi.org/10.1007/s00134-021-06506-y

Fatsis-Kavalopoulos, N., Roemhild, R., Tang, P.-C., Kreuger, J., & Andersson, D. I. (2020). CombiANT: Antibiotic interaction testing made easy. PLOS Biology, 18(e3000856). https://doi.org/10.1371/journal.pbio.3000856

Fleming, J., Marvel, S. W., Supak, S., Motsinger-Reif, A. A., & Reif, D. M. (2022). ToxPi*GIS toolkit: Creating, viewing, and sharing integrative visualizations for geospatial data using ArcGIS. Journal of Exposure Science & Environmental Epidemiology, 32(6), 900–907. https://doi.org/10.1038/s41370-022-00433-w

Gammel, N., Ross, T. L., Lewis, S., Olson, M., Henciak, S., Harris, R., et al. (2021). Comparison of an automated plate assessment system (APAS Independence) and artificial intelligence (AI) to manual plate reading of methicillin-resistant and methicillin-susceptible Staphylococcus aureus CHROMagar surveillance cultures. Journal of Clinical Microbiology, 59(e00971-21). https://doi.org/10.1128/JCM.00971-21

Gao, S., & Wang, H. (2022). Scenario prediction of public health emergencies using infectious disease dynamics model and dynamic Bayes. Future Generation Computer Systems, 127, 334–346. https://doi.org/10.1016/j.future.2021.09.028

GBD Antimicrobial Resistance Collaborators. (2019). Global mortality associated with 33 bacterial pathogens in 2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 400(10369), 2221–2248. https://doi.org/10.1016/S0140-6736(22)02185-7

Goodman, K. E., Lessler, J., Cosgrove, S. E., Harris, A. D., Lautenbach, E., Han, J. H., et al. (2016). A clinical decision tree to predict whether a bacteremic patient is infected with an extended-spectrum β-lactamase-producing organism. Clinical Infectious Diseases, 63(7), 896–903. https://doi.org/10.1093/cid/ciw425

Goodswen, S. J., Barratt, J. L. N., Kennedy, P. J., Kaufer, A., Calarco, L., & Ellis, J. T. (2021). Machine learning and applications in microbiology. FEMS Microbiology Reviews, 45(1), fuab015. https://doi.org/10.1093/femsre/fuab015

Gurvic, D., Leach, A. G., & Zachariae, U. (2022). Data-driven derivation of molecular substructures that enhance drug activity in gram-negative bacteria. Journal of Medicinal Chemistry, 65(9), 6088–6099. https://doi.org/10.1021/acs.jmedchem.1c01984

Han, N., Oh, O. H., Oh, J., Kim, Y., Lee, Y., Cha, W. C., et al. (2024). The application of knowledge-based clinical decision support systems to detect antibiotic allergy. Antibiotics (Basel), 13(3), 244. https://doi.org/10.3390/antibiotics13030244

Heinson, A. I., Woelk, C. H., & Newell, M.-L. (2015). The promise of reverse vaccinology. International Health, 7(2), 85–89. https://doi.org/10.1093/inthealth/ihv002

Hellmich, T. R., Clements, C. M., El-Sherif, N., Pasupathy, K. S., Nestler, D. M., Boggust, A., et al. (2017). Contact tracing with a real-time location system: A case study of increasing relative effectiveness in an emergency department. American Journal of Infection Control, 45(12), 1308–1311. https://doi.org/10.1016/j.ajic.2017.08.014

Hirosawa, T., Harada, Y., Yokose, M., Sakamoto, T., Kawamura, R., & Shimizu, T. (2023). Diagnostic accuracy of differential-diagnosis lists generated by generative pretrained transformer 3 chatbot for clinical vignettes with common chief complaints: A pilot study. International Journal of Environmental Research and Public Health, 20(4), 3378. https://doi.org/10.3390/ijerph20043378

Ho, C.-S., Jean, N., Hogan, C. A., Blackmon, L., Jeffrey, S. S., Holodniy, M., et al. (2019). Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning. Nature Communications, 10, 4927. https://doi.org/10.1038/s41467-019-12898-9

Horvath, L., Hänselmann, S., Mannsperger, H., Degenhardt, S., Last, K., Zimmermann, S., et al. (2020). Machine-assisted interpretation of auramine stains substantially increases throughput and sensitivity of microscopic tuberculosis diagnosis. Tuberculosis (Edinburgh), 125, 101993. https://doi.org/10.1016/j.tube.2020.101993

Howard, A., Aston, S., Gerada, A., Reza, N., Bincalar, J., Mwandumba, H., et al. (2024). Antimicrobial learning systems: An implementation blueprint for artificial intelligence to tackle antimicrobial resistance. The Lancet Digital Health, 6, e79–e86. https://doi.org/10.1016/S2589-7500(23)00221-2

Hsu, K.-W., Lee, W.-B., You, H.-L., Lee, M. S., & Lee, G.-B. (2021). An automated and portable antimicrobial susceptibility testing system for urinary tract infections. Lab on a Chip, 21, 755–763. https://doi.org/10.1039/d0lc01315c

Hummel, P., & Braun, M. (2020). Just data? Solidarity and justice in data-driven medicine. Life Sciences, Society and Policy, 16, 8. https://doi.org/10.1186/s40504-020-00101-7

Jiang, Y., Luo, J., Huang, D., Liu, Y., & Li, D. (2022). Machine learning advances in microbiology: A review of methods and applications. Frontiers in Microbiology, 13, 925454. https://doi.org/10.3389/fmicb.2022.925454

Keith, M., Park de la Torriente, A., Chalka, A., Vallejo-Trujillo, A., McAteer, S. P., Paterson, G. K., et al. (2024). Predictive phage therapy for Escherichia coli urinary tract infections: Cocktail selection for therapy based on machine learning models. Proceedings of the National Academy of Sciences of the United States of America, 121, e2313574121. https://doi.org/10.1073/pnas.2313574121

Khanna, D., & Rana, P. S. (2019). Ensemble technique for prediction of T-cell Mycobacterium tuberculosis epitopes. Interdisciplinary Sciences, 11, 611–627. https://doi.org/10.1007/s12539-018-0309-0

Kleandrova, V. V., & Speck-Planche, A. (2020). The QSAR paradigm in fragment-based drug discovery: From the virtual generation of target inhibitors to multi-scale modeling. Mini Reviews in Medicinal Chemistry, 20, 1357–1374. https://doi.org/10.2174/1389557520666200204123156

Kulshrestha, M., Tiwari, M., & Tiwari, V. (2024). Bacteriophage therapy against ESKAPE bacterial pathogens: Current status, strategies, challenges, and future scope. Microbial Pathogenesis, 186, 106467. https://doi.org/10.1016/j.micpath.2023.106467

Laliwala, A., Svechkarev, D., Sadykov, M. R., Endres, J., Bayles, K. W., & Mohs, A. M. (2022). Simpler procedure and improved performance for pathogenic bacteria analysis with a paper-based ratiometric fluorescent sensor array. Analytical Chemistry, 94, 2615–2624. https://doi.org/10.1021/acs.analchem.1c05021

Lane, T. R., Urbina, F., Rank, L., Gerlach, J., Riabova, O., Lepioshkin, A., et al. (2022). Machine learning models for Mycobacterium tuberculosis in vitro activity: Prediction and target visualization. Molecular Pharmaceutics, 19, 674–689. https://doi.org/10.1021/acs.molpharmaceut.1c00791

Langford, B. J., Branch-Elliman, W., Nori, P., Marra, A. R., & Bearman, G. (2024). Confronting the disruption of the infectious diseases workforce by artificial intelligence: What this means for us and what we can do about it. Open Forum Infectious Diseases, 11, ofae053. https://doi.org/10.1093/ofid/ofae053

Larentzakis, A., & Lygeros, N. (2021). Artificial intelligence (AI) in medicine as a strategic valuable tool. Pan African Medical Journal, 38, 184. https://doi.org/10.11604/pamj.2021.38.184.28197

Larsen, P. E., Collart, F. R., & Dai, Y. (2014). Using metabolomic and transportomic modeling and machine learning to identify putative novel therapeutic targets for antibiotic-resistant pseudomonad infections. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 314–317. https://doi.org/10.1109/EMBC.2014.6943592

Lesosky, M., McGeer, A., Simor, A., Green, K., Low, D. E., & Raboud, J. (2011). Effect of patterns of transferring patients among healthcare institutions on rates of nosocomial methicillin-resistant Staphylococcus aureus transmission: A Monte Carlo simulation. Infection Control & Hospital Epidemiology, 32, 136–147. https://doi.org/10.1086/657945

Li, R., Shen, M., Liu, H., Bai, L., & Zhang, L. (2023). Do infrared thermometers hold promise for an effective early warning system for emerging respiratory infectious diseases? JMIR Formative Research, 7, e42548. https://doi.org/10.2196/42548

Liu, W., Ying, N., Mo, Q., Li, S., Shao, M., Sun, L., et al. (2021). Machine learning for identifying resistance features of Klebsiella pneumoniae using whole-genome sequence single nucleotide polymorphisms. Journal of Medical Microbiology, 70. https://doi.org/10.1099/jmm.0.001474

Lu, J., Chen, J., Liu, C., Zeng, Y., Sun, Q., Li, J., et al. (2022). Identification of antibiotic resistance and virulence-encoding factors in Klebsiella pneumoniae by Raman spectroscopy and deep learning. Microbial Biotechnology, 15, 1270–1280. https://doi.org/10.1111/1751-7915.13960

Mandal, S., Tannert, A., Löffier, B., Neugebauer, U., & Silva, L. B. (2024). Findaureus: An open-source application for locating Staphylococcus aureus in fluorescence-labelled infected bone tissue slices. PLoS ONE, 19, e0296854. https://doi.org/10.1371/journal.pone.0296854

Mc Cord-De Iaco, K. A., Gesualdo, F., Pandolfi, E., Croci, I., & Tozzi, A. E. (2023). Machine learning clinical decision support systems for surveillance: A case study on pertussis and RSV in children. Frontiers in Pediatrics, 11, 1112074. https://doi.org/10.3389/fped.2023.1112074

McGregor, J. C., Weekes, E., Forrest, G. N., Standiford, H. C., Perencevich, E. N., Furuno, J. P., et al. (2006). Impact of a computerized clinical decision support system on reducing inappropriate antimicrobial use: A randomized controlled trial. Journal of the American Medical Informatics Association, 13, 378–384. https://doi.org/10.1197/jamia.M2049

Mintz, Y., & Brodie, R. (2019). Introduction to artificial intelligence in medicine. Minimally Invasive Therapy & Allied Technologies, 28, 73–81. https://doi.org/10.1080/13645706.2019.1575882

Nakar, A., Pistiki, A., Ryabchykov, O., Bocklitz, T., Rösch, P., & Popp, J. (2022). Detection of multi-resistant clinical strains of E. coli with Raman spectroscopy. Analytical and Bioanalytical Chemistry, 414, 1481–1492. https://doi.org/10.1007/s00216-021-03800-y

Nguyen, M., Brettin, T., Long, S. W., Musser, J. M., Olsen, R. J., Olson, R., et al. (2018). Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. Scientific Reports, 8(1), 421. https://doi.org/10.1038/s41598-017-18972-w

Njage, P. M. K., Leekitcharoenphon, P., & Hald, T. (2019). Improving hazard characterization in microbial risk assessment using next generation sequencing data and machine learning: Predicting clinical outcomes in shigatoxigenic Escherichia coli. International Journal of Food Microbiology, 292, 72–82. https://doi.org/10.1016/j.ijfoodmicro.2018.11.016

Oh, J., Makar, M., Fusco, C., McCaffrey, R., Rao, K., Ryan, E. E., et al. (2018). A generalizable, data-driven approach to predict daily risk of Clostridium difficile infection at two large academic health centers. Infection Control & Hospital Epidemiology, 39(4), 425–433. https://doi.org/10.1017/ice.2018.16

Ohkusu, K. (2000). Cost-effective and rapid presumptive identification of gram-negative bacilli in routine urine, pus, and stool cultures: Evaluation of the use of CHROMagar orientation medium in conjunction with simple biochemical tests. Journal of Clinical Microbiology, 38(12), 4586–4592. https://doi.org/10.1128/JCM.38.12.4586-4592.2000

Paquin, P., Durmort, C., Paulus, C., Vernet, T., Marcoux, P. R., & Morales, S. (2022). Spatio-temporal-based deep learning for rapid detection and identification of bacterial colonies through lens-free microscopy time-lapses. PLOS Digital Health, 1(1), e0000122. https://doi.org/10.1371/journal.pdig.0000122

Pérez-Sancho, M., Vela, A. I., Horcajo, P., Ugarte-Ruiz, M., Domínguez, L., Fernández-Garayzábal, J. F., et al. (2018). Rapid differentiation of Staphylococcus aureus subspecies based on MALDI-TOF MS profiles. Journal of Veterinary Diagnostic Investigation, 30(6), 813–820. https://doi.org/10.1177/1040638718805537

Periasamy, A. (2014). Advanced light microscopy. Methods, 66(1), 121–123. https://doi.org/10.1016/j.ymeth.2014.03.011

Pfeiffer, D. U., & Stevens, K. B. (2015). Spatial and temporal epidemiological analysis in the big data era. Preventive Veterinary Medicine, 122(1–2), 213–220. https://doi.org/10.1016/j.prevetmed.2015.05.012

Qiu, J., Nie, W., Ding, H., Dai, J., Wei, Y., Li, D., et al. (2024). PB-LKS: A python package for predicting phage-bacteria interaction through local K-mer strategy. Briefings in Bioinformatics, 25, bbae010. https://doi.org/10.1093/bib/bbae010

Rahman, M. M., Tasnim, M., Li, M., Devadas, H., & Mamoon, M. Y. (2024). Necrotizing Pancreatitis Due to Very High Triglyceride Level: A Case Report. Cureus, 16(9), e69761. https://doi.org/10.7759/cureus.69761

Ramachandran, P. S., Ramesh, A., Creswell, F. V., Wapniarski, A., Narendra, R., Quinn, C. M., et al. (2022). Integrating central nervous system metagenomics and host response for diagnosis of tuberculosis meningitis and its mimics. Nature Communications, 13(1), 1675. https://doi.org/10.1038/s41467-022-29353-x

Rapún-Araiz, B., Sorzabal-Bellido, I., Asensio-López, J., Lázaro-Díez, M., Ariz, M., Sobejano de la Merced, C., et al. (2023). In vitro modeling of polyclonal infection dynamics within the human airways by Haemophilus influenzae differential fluorescent labeling. Microbiology Spectrum, 11(3), e00993-23. https://doi.org/10.1128/spectrum.00993-23

Rawal, K., Sinha, R., Abbasi, B. A., Chaudhary, A., Nath, S. K., Kumari, P., et al. (2021). Identification of vaccine targets in pathogens and design of a vaccine using computational approaches. Scientific Reports, 11(1), 17626. https://doi.org/10.1038/s41598-021-96863-x

Rees, C., & Müller, B. (2022). All that glitters is not gold: Trustworthy and ethical AI principles. AI and Ethics, 3(3), 1241–1254. https://doi.org/10.1007/s43681-022-00232-x

Rhodes, N. J., Rohani, R., Yarnold, P. R., Pawlowski, A. E., Malczynski, M., Qi, C., et al. (2023). Machine learning to stratify methicillin-resistant Staphylococcus aureus risk among hospitalized patients with community-acquired pneumonia. Antimicrobial Agents and Chemotherapy, 67(1), e01023-22. https://doi.org/10.1128/aac.01023-22

Rodrigues Lopes, I., Alcantara, L. M., Silva, R. J., Josse, J., Vega, E. P., Cabrerizo, A. M., et al. (2022). Microscopy-based phenotypic profiling of infection by Staphylococcus aureus clinical isolates reveals intracellular lifestyle as a prevalent feature. Nature Communications, 13, 7174. https://doi.org/10.1038/s41467-022-34790-9

Salam, M. T., Bari, K. F., & others. (2024). Emergence of antibiotic-resistant infections in ICU patients. Journal of Angiotherapy, 8(5), 1–9. https://doi.org/10.25163/angiotherapy.859560

Santa Maria, J. P., Park, Y., Yang, L., Murgolo, N., Altman, M. D., Zuck, P., et al. (2017). Linking high-throughput screens to identify MoAs and novel inhibitors of Mycobacterium tuberculosis dihydrofolate reductase. ACS Chemical Biology, 12, 2448–2456. https://doi.org/10.1021/acschembio.7b00468

Schwartz, I. S., Link, K. E., Daneshjou, R., & Cortés-Penfield, N. (2024). Black box warning: Large language models and the future of infectious diseases consultation. Clinical Infectious Diseases, 78, 860–866. https://doi.org/10.1093/cid/ciad633

Senescau, A., Kempowsky, T., Bernard, E., Messier, S., Besse, P., Fabre, R., & François, J. M. (2018). Innovative DendrisChips® technology for a syndromic approach of in vitro diagnosis: Application to the respiratory infectious diseases. Diagnostics, 8(4), 77. https://doi.org/10.3390/diagnostics8040077

Shen, Y., Yuan, K., Chen, D., Colloc, J., Yang, M., Li, Y., et al. (2018). An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription. Artificial Intelligence in Medicine, 86, 20–32. https://doi.org/10.1016/j.artmed.2018.01.003

Sherry, N. L., Horan, K. A., Ballard, S. A., Gon?alves da Silva, A., Gorrie, C. L., Schultz, M. B., et al. (2023). An ISO-certified genomics workflow for identification and surveillance of antimicrobial resistance. Nature Communications, 14, 60. https://doi.org/10.1038/s41467-022-35713-4

Stracy, M., Snitser, O., Yelin, I., Amer, Y., Parizade, M., Katz, R., et al. (2022). Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections. Science, 375, 889–894. https://doi.org/10.1126/science.abg9868

Tagliaferri, T. L., Jansen, M., & Horz, H.-P. (2019). Fighting pathogenic bacteria on two fronts: Phages and antibiotics as a combined strategy. Frontiers in Cellular and Infection Microbiology, 9, 22. https://doi.org/10.3389/fcimb.2019.00022

Tilton, C. S., & Johnson, S. W. (2019). Development of a risk prediction model for hospital-onset Clostridium difficile infection in patients receiving systemic antibiotics. American Journal of Infection Control, 47, 280–284. https://doi.org/10.1016/j.ajic.2018.08.021

Ting Sim, J. Z., Fong, Q. W., Huang, W., & Tan, C. H. (2023). Machine learning in medicine: What clinicians should know. Singapore Medical Journal, 64, 91–97. https://doi.org/10.11622/smedj.2021054

Tufael, M., Rahman, M. M., & others. (2024). Combined biomarkers for early diagnosis of hepatocellular carcinoma. Journal of Angiotherapy, 8(5), 1–12. https://doi.org/10.25163/angiotherapy.859665

Váradi, L., Luo, J. L., Hibbs, D. E., Perry, J. D., Anderson, R. J., Orenga, S., et al. (2017). Methods for the detection and identification of pathogenic bacteria: Past, present, and future. Chemical Society Reviews, 46, 4818–4832. https://doi.org/10.1039/c6cs00693k

Viertel, T. M., Ritter, K., & Horz, H.-P. (2014). Viruses versus bacteria: Novel approaches to phage therapy as a tool against multidrug-resistant pathogens. Journal of Antimicrobial Chemotherapy, 69, 2326–2336. https://doi.org/10.1093/jac/dku173

Villarroel, J., Kleinheinz, K. A., Jurtz, V. I., Zschach, H., Lund, O., Nielsen, M., et al. (2016). HostPhinder: A phage host prediction tool. Viruses, 8(116). https://doi.org/10.3390/v8050116

Volynets, G. P., Usenko, M. O., Gudzera, O. I., Starosyla, S. A., Balanda, A. O., Syniugin, A. R., et al. (2022). Identification of dual-targeted Mycobacterium tuberculosis aminoacyl-tRNA synthetase inhibitors using machine learning. Future Medicinal Chemistry, 14(1223–1237). https://doi.org/10.4155/fmc-2022-0085

Waddington, C., Carey, M. E., Boinett, C. J., Higginson, E., Veeraraghavan, B., & Baker, S. (2022). Exploiting genomics to mitigate the public health impact of antimicrobial resistance. Genome Medicine, 14(15). https://doi.org/10.1186/s13073-022-01020-2

Waddington, C., Carey, M. E., Boinett, C. J., Higginson, E., Veeraraghavan, B., & Baker, S. (2022). Exploiting genomics to mitigate the public health impact of antimicrobial resistance. Genome Medicine, 14, 15. https://doi.org/10.1186/s13073-022-01020-2

Walsh, T. R., Gales, A. C., Laxminarayan, R., & Dodd, P. C. (2023). Antimicrobial resistance: Addressing a global threat to humanity. PLOS Medicine, 20, e1004264. https://doi.org/10.1371/journal.pmed.1004264

Walsh, T. R., Gales, A. C., Laxminarayan, R., & Dodd, P. C. (2023). Antimicrobial resistance: Addressing a global threat to humanity. PLoS Medicine, 20, e1004264. https://doi.org/10.1371/journal.pmed.1004264

Wang, H., Ceylan Koydemir, H., Qiu, Y., Bai, B., Zhang, Y., Jin, Y., et al. (2020). Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning. Light: Science & Applications, 9(118). https://doi.org/10.1038/s41377-020-00358-9

Wang, H., Ceylan Koydemir, H., Qiu, Y., Bai, B., Zhang, Y., Jin, Y., et al. (2020). Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning. Light: Science & Applications, 9, 118. https://doi.org/10.1038/s41377-020-00358-9

Wang, H.-Y., Chung, C.-R., Wang, Z., Li, S., Chu, B.-Y., Horng, J.-T., et al. (2021). A large-scale investigation and identification of methicillin-resistant Staphylococcus aureus based on peaks binning of matrix-assisted laser desorption ionization-time of flight MS spectra. Briefings in Bioinformatics, 22, bbaa138. https://doi.org/10.1093/bib/bbaa138

Wang, H.-Y., Chung, C.-R., Wang, Z., Li, S., Chu, B.-Y., Horng, J.-T., et al. (2021). A large-scale investigation and identification of methicillin-resistant Staphylococcus aureus based on peaks binning of matrix-assisted laser desorption ionization-time of flight MS spectra. Briefings in Bioinformatics, 22, bbaa138. https://doi.org/10.1093/bib/bbaa138

Wang, M., Wei, Z., Jia, M., Chen, L., & Ji, H. (2022). Deep learning model for multi-classification of infectious diseases from unstructured electronic medical records. BMC Medical Informatics and Decision Making, 22(41). https://doi.org/10.1186/s12911-022-01776-y

Wang, M., Wei, Z., Jia, M., Chen, L., & Ji, H. (2022). Deep learning model for multi-classification of infectious diseases from unstructured electronic medical records. BMC Medical Informatics and Decision Making, 22, 41. https://doi.org/10.1186/s12911-022-01776-y

Wells, J., Grant, R., Chang, J., & Kayyali, R. (2021). Evaluating the usability and acceptability of a geographical information system (GIS) prototype to visualize socio-economic and public health data. BMC Public Health, 21(2151). https://doi.org/10.1186/s12889-021-12072-1

Wells, J., Grant, R., Chang, J., & Kayyali, R. (2021). Evaluating the usability and acceptability of a geographical information system (GIS) prototype to visualize socio-economic and public health data. BMC Public Health, 21, 2151. https://doi.org/10.1186/s12889-021-12072-1

Wieser, A., Schneider, L., Jung, J., & Schubert, S. (2012). MALDI-TOF MS in microbiological diagnostics—Identification of microorganisms and beyond (mini-review). Applied Microbiology and Biotechnology, 93, 965–974. https://doi.org/10.1007/s00253-011-3783-4

Wieser, A., Schneider, L., Jung, J., & Schubert, S. (2012). MALDI-TOF MS in microbiological diagnostics: Identification of microorganisms and beyond (mini review). Applied Microbiology and Biotechnology, 93, 965–974. https://doi.org/10.1007/s00253-011-3783-4

Wilson, M. L. (2015). Diagnostic microbiology: The accelerating transition from culture-based to molecular-based methods. American Journal of Clinical Pathology, 143, 766–767. https://doi.org/10.1309/AJCPIC9GPLHCV1NT

Wilson, M. L. (2015). Diagnostic microbiology: The accelerating transition from culture-based to molecular-based methods. American Journal of Clinical Pathology, 143, 766–767. https://doi.org/10.1309/AJCPIC9GPLHCV1NT

Wong, F., De La Fuente-Nunez, C., & Collins, J. J. (2023). Leveraging artificial intelligence in the fight against infectious diseases. Science, 381, 164–170. https://doi.org/10.1126/science.adh1114

Wong, F., De La Fuente-Nunez, C., & Collins, J. J. (2023). Leveraging artificial intelligence in the fight against infectious diseases. Science, 381, 164–170. https://doi.org/10.1126/science.adh1114

Yan, Y., Chen, C., Liu, Y., Zhang, Z., Xu, L., & Pu, K. (2021). Application of machine learning for the prediction of etiological types of classic fever of unknown origin. Frontiers in Public Health, 9, 800549. https://doi.org/10.3389/fpubh.2021.800549

PDF
Full Text
Export Citation

View Dimensions


View Plumx



View Altmetric



5
Save
0
Citation
173
View
0
Share