Machine learning models and classification algorithms in the diagnosis of vestibular migraine: A systematic review and meta‐analysis.
Objectives: To perform a systematic review and meta‐analysis to evaluate the effectiveness of machine learning (ML) algorithms in the diagnosis of vestibular migraine. Background: Due to the absence of defined biomarkers for diagnosing vestibular migraine (VM), it is valuable to determine which clin...
| Publicado en: | Headache: The Journal of Head & Face Pain Vol. 65; no. 4; pp. 695 - 709 |
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| Autores principales: | , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184143048&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184143048 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00178748 4AP jtl: Headache: The Journal of Head & Face Pain issn: 00178748 maglogo: Y pubinfo: dt: Apr2025 vid: 65 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 184143048 183624458 184143048 184143048 10.1111/head.14924 184143048 ppf: 695 ppct: 14 formats: tig: atl: Machine learning models and classification algorithms in the diagnosis of vestibular migraine: A systematic review and meta‐analysis. aug: au: Suarez‐Barcena, Pablo D. Parra‐Perez, Alberto M. Martín‐Lagos, Juan Gallego‐Martinez, Alvaro Lopez‐Escámez, Jose A. Perez‐Carpena, Patricia affil: Department of Otolaryngology, Hospital Universitario San Cecilio, Instituto de Investigación Biosanitaria, Ibs.GRANADA, Granada, Spain sug: subj: Vestibular Migraine Diagnosis Machine Learning Methods Prediction Models Classification Algorithms Methods Diagnosis, Computer Assisted Methods Human Funding Source Systematic Review Meta Analysis PubMed Artificial Intelligence Quality Assessment Physical Examination Vestibular Function Tests Descriptive Statistics Confidence Intervals ROC Curve Checklists ab: Objectives: To perform a systematic review and meta‐analysis to evaluate the effectiveness of machine learning (ML) algorithms in the diagnosis of vestibular migraine. Background: Due to the absence of defined biomarkers for diagnosing vestibular migraine (VM), it is valuable to determine which clinical, physical, and exploratory information is most crucial to diagnosing this disease. The use of artificial intelligence tools could streamline this process. Methods: This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines and searched for records from PubMed, Scopus, and Web of Science. Observational (case–control and cohort) studies were included to assess the ability of artificial intelligence (AI) to distinguish VM from other vestibular disorders. Risk of bias and applicability concerns were assessed using the Quality Assessment of Diagnostic Accuracy Studies‐AI tool. Results: A total of 14 articles were included in the systematic review, and 10 were eligible for meta‐analysis. The main inputs included for the ML algorithms were anamnesis (medical history), physical examination, results from audiological and vestibular tests, and imaging. The global sensitivity was 0.85 (95% confidence interval [CI] 0.73–0.92, I2 = 96%), while the global specificity was 0.89 (95% CI 0.84–0.93, I2 = 95%). The pooled diagnostic odds ratio was 48.15 (95% CI 17.64–131.43, I2 = 97%). Using the bivariate model, the area under the curve and for the summary receiver operating characteristic curve, using the 10 available studies, was 0.94 (95% CI 0.86–0.96). Conclusion: Machine learning algorithms could be used as effective tools for the diagnosis process in VM. The use of models trained with three to four inputs yield the highest accuracy, compared to other strategies. However, the design and validation of these studies could be improved to ensure the reproducibility and generalizability of results. Plain Language Summary: The diagnosis of vestibular migraine (VM) is based on a list of symptoms reported by patients, but this can sometimes lead to delays in diagnosis. We conducted a systematic review and meta‐analysis to understand whether machine learning tools can help to diagnose patients with VM based on clinical and physical information, or other complementary tests. We found that combining clinical information and machine learning methods could facilitate and shorten the diagnostic process of VM. pubtype: Academic Journal doctype: meta analysis research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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