AI and Lyme Disease: Pioneering Advances in Diagnostic Accuracy
DOI:
https://doi.org/10.12775/JEHS.2024.73.51697Keywords
Artificial Intelligence, Lyme Disease, Machine LearningAbstract
Introduction and Purpose: Accurate diagnosis of Lyme disease remains challenging due to its varied manifestations and the limitations of current diagnostic tests. This review examines the emerging role of artificial intelligence (AI) in enhancing the diagnostic accuracy for Lyme disease, aiming to understand how these technologies can be integrated into clinical practice.
State of Knowledge: AI and machine learning techniques are increasingly applied to improve diagnostic processes. In Lyme disease, AI models have been developed to identify patterns in clinical data, enhancing early detection and accuracy. Studies have focused on using AI to interpret complex serological results and clinical symptoms more effectively than traditional methods. Additionally, AI has been utilized to analyze geographical and epidemiological data to predict Lyme disease risk areas, aiding in preventive strategies.
Summary: AI holds significant promise in transforming Lyme disease diagnostics by increasing the speed and accuracy of detection. These technologies not only help in overcoming the limitations of current serological testing but also provide a framework for predictive analytics in epidemiology. As AI models continue to evolve, their integration into healthcare systems requires careful consideration of ethical implications and validation on broader scales. Future research should focus on refining AI algorithms, improving data inclusivity, and enhancing interoperability with existing medical systems to fully realize AI's potential in battling Lyme disease.
References
Boyce RM et al. Errors in Diagnostic Test Use and Interpretation Contribute to the High Number of Lyme Disease Referrals in a Low-Incidence State. Open Forum Infect Dis. 2020 Jan 11;7(1):ofaa009. doi: 10.1093/ofid/ofaa009. PMID: 31988970; PMCID: PMC6976340.
Berner ES, Graber ML. Overconfidence as a Cause of Diagnostic Error in Medicine. Am J Med. 2008;121(5):S2-S23. doi:10.1016/j.amjmed.2008.01.001
Ross Russell AL, Dryden M, Pinto AA, et alLyme disease: diagnosis and management Practical Neurology Published Online First: 03 October 2018. doi: 10.1136/practneurol-2018-001998
Reddy, Panga Jaipal et al. “Borrelia Peptide Atlas: A proteome resource of common Borrelia burgdorferi isolates for Lyme research.” bioRxiv : the preprint server for biology 2023.06.16.545244. 16 Jun. 2023, doi:10.1101/2023.06.16.545244. Preprint.
Bamm, Vladimir V et al. “Lyme Disease Frontiers: Reconciling Borrelia Biology and Clinical Conundrums.” Pathogens (Basel, Switzerland) vol. 8,4 299. 16 Dec. 2019, doi:10.3390/pathogens8040299
Eugene D. Shapiro, Michael A. Gerber, Lyme Disease, Clinical Infectious Diseases, Volume 31, Issue 2, August 2000, Pages 533–542, https://doi.org/10.1086/313982
Tatum, Randi. and Anthony L. Pearson-Shaver. “Borrelia Burgdorferi.” StatPearls, StatPearls Publishing, 17 July 2023.
Steere, Allen C et al. “Lyme borreliosis.” Nature reviews. Disease primers vol. 2 16090. 15 Dec. 2016, doi:10.1038/nrdp.2016.90
Wong, Katelyn H et al. “A Review of Post-treatment Lyme Disease Syndrome and Chronic Lyme Disease for the Practicing Immunologist.” Clinical reviews in allergy & immunology vol. 62,1 (2022): 264-271. doi:10.1007/s12016-021-08906-w
David R. Snydman, Lyme disease, Medicine, Volume 45, Issue 12, 2017, Pages 743-746, ISSN 1357-3039, https://doi.org/10.1016/j.mpmed.2017.09.005.
Baker, Phillip J. “Is It Possible to Make a Correct Diagnosis of Lyme Disease on Symptoms Alone? Review of Key Issues and Public Health Implications.” The American journal of medicine vol. 132,10 (2019): 1148-1152. doi:10.1016/j.amjmed.2019.04.001
Bratton, Robert L et al. “Diagnosis and treatment of Lyme disease.” Mayo Clinic proceedings vol. 83,5 (2008): 566-71. doi:10.4065/83.5.566
Schoen, Robert T. “Lyme disease: diagnosis and treatment.” Current opinion in rheumatology vol. 32,3 (2020): 247-254. doi:10.1097/BOR.0000000000000698
Branda, John A, and Allen C Steere. “Laboratory Diagnosis of Lyme Borreliosis.” Clinical microbiology reviews vol. 34,2 e00018-19. 27 Jan. 2021, doi:10.1128/CMR.00018-19
Hatchette, T F et al. “Lyme disease: clinical diagnosis and treatment.” Canada communicable disease report = Releve des maladies transmissibles au Canada vol. 40,11 194-208. 29 May. 2014, doi:10.14745/ccdr.v40i11a01
Kobayashi et al. “Diagnostic Testing for Lyme Disease.” Infectious disease clinics of North America vol. 36,3 (2022): 605-620. doi:10.1016/j.idc.2022.04.001
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi:10.1136/bmj.n71. PMID: 33782057; PMCID: PMC8005924.
Forrest, Iain S et al. “Cholesterol Contributes to Risk, Severity, and Machine Learning-Driven Diagnosis of Lyme Disease.” Clinical infectious diseases : an official publication of the Infectious Diseases Society of America vol. 77,6 (2023): 839-847. doi:10.1093/cid/ciad307
Boligarla, S., Laison, E.K.E., Li, J. et al. Leveraging machine learning approaches for predicting potential Lyme disease cases and incidence rates in the United States using Twitter. BMC Med Inform Decis Mak 23, 217 (2023). https://doi.org/10.1186/s12911-023-02315-z
Donta, Sam T. “Issues in the diagnosis and treatment of lyme disease.” The open neurology journal vol. 6 (2012): 140-5. doi:10.2174/1874205X01206010140
Elkhadrawi, Mahmoud et al. “Improving Lyme disease testing with data driven test design in pediatrics.” Journal of pathology informatics vol. 14 100300. 16 Feb. 2023, doi:10.1016/j.jpi.2023.100300
Marques, Adriana R et al. “Comparison of Lyme Disease in the United States and Europe.” Emerging infectious diseases vol. 27,8 (2021): 2017-2024. doi:10.3201/eid2708.204763
Ichikawa, Osamu et al. “Lyme Disease Patient Trajectories Learned from Electronic Medical Data for Stratification of Disease Risk and Therapeutic Response.” Scientific reports vol. 9,1 4460. 14 Mar. 2019, doi:10.1038/s41598-019-41128-x
Radesich, Cinzia et al. “Lyme Carditis: From Pathophysiology to Clinical Management.” Pathogens (Basel, Switzerland) vol. 11,5 582. 15 May. 2022, doi:10.3390/pathogens11050582
Govil, Swati et al. “Common Neurologic Features of Lyme Disease That May Present to a Rheumatologist.” Pathogens (Basel, Switzerland) vol. 12,4 576. 9 Apr. 2023, doi:10.3390/pathogens12040576
Bobe, Jason R et al. “Recent Progress in Lyme Disease and Remaining Challenges.” Frontiers in medicine vol. 8 666554. 18 Aug. 2021, doi:10.3389/fmed.2021.666554
Garcia-Marti, Irene et al. “Modelling tick bite risk by combining random forests and count data regression models.” PloS one vol. 14,12 e0216511. 10 Dec. 2019, doi:10.1371/journal.pone.0216511
Zhang, Yudong et al. “An MR brain images classifier system via particle swarm optimization and kernel support vector machine.” TheScientificWorldJournal vol. 2013 130134. 16 Sep. 2013, doi:10.1155/2013/130134
Laison, Elda Kokoe Elolo et al. “Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis.” Journal of medical Internet research vol. 25 e47014. 16 Oct. 2023, doi:10.2196/47014
Joung, Hyou-Arm et al. “Point-of-Care Serodiagnostic Test for Early-Stage Lyme Disease Using a Multiplexed Paper-Based Immunoassay and Machine Learning.” ACS nano vol. 14,1 (2020): 229-240. doi:10.1021/acsnano.9b08151
Servellita, V., Bouquet, J., Rebman, A. et al. A diagnostic classifier for gene expression-based identification of early Lyme disease. Commun Med 2, 92 (2022). https://doi.org/10.1038/s43856-022-00127-2
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