Machine learning models and classification algorithms in the diagnosis of vestibular migraine: A systematic review and meta-analysis.

Abstract

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.