*Result*: Machine learning models and classification algorithms in the diagnosis of vestibular migraine: A systematic review and meta-analysis.
Title:
Machine learning models and classification algorithms in the diagnosis of vestibular migraine: A systematic review and meta-analysis.
Authors:
Suarez-Barcena PD; Department of Otolaryngology, Hospital Universitario San Cecilio, Instituto de Investigación Biosanitaria, Ibs.GRANADA, Granada, Spain., Parra-Perez AM; Otology and Neurotology Group CTS495, Instituto de Investigación Biosanitaria, ibs.GRANADA, Universidad de Granada, Granada, Spain.; Sensorineural Pathology Programme, Centro de Investigación Biomédica en Red en Enfermedades Raras, CIBERER, Madrid, Spain.; Division of Otolaryngology, Department of Surgery, Instituto de Investigación Biosanitaria, ibs.GRANADA, Universidad de Granada, Granada, Spain., Martín-Lagos J; Department of Otolaryngology, Hospital Universitario San Cecilio, Instituto de Investigación Biosanitaria, Ibs.GRANADA, Granada, Spain.; Otology and Neurotology Group CTS495, Instituto de Investigación Biosanitaria, ibs.GRANADA, Universidad de Granada, Granada, Spain.; Sensorineural Pathology Programme, Centro de Investigación Biomédica en Red en Enfermedades Raras, CIBERER, Madrid, Spain., Gallego-Martinez A; Otology and Neurotology Group CTS495, Instituto de Investigación Biosanitaria, ibs.GRANADA, Universidad de Granada, Granada, Spain.; Sensorineural Pathology Programme, Centro de Investigación Biomédica en Red en Enfermedades Raras, CIBERER, Madrid, Spain.; Genome Biology Department, Centro Andaluz de Biología Molecular y Medicina Regenerativa (CABIMER), Consejo Superior de Investigaciones Científicas-Universidad de Sevilla-Universidad Pablo de Olavide (CSIC-USE-UPO), Av. Americo Vespucio, Seville, Spain., Lopez-Escámez JA; Otology and Neurotology Group CTS495, Instituto de Investigación Biosanitaria, ibs.GRANADA, Universidad de Granada, Granada, Spain.; Sensorineural Pathology Programme, Centro de Investigación Biomédica en Red en Enfermedades Raras, CIBERER, Madrid, Spain.; Division of Otolaryngology, Department of Surgery, Instituto de Investigación Biosanitaria, ibs.GRANADA, Universidad de Granada, Granada, Spain.; Meniere's Disease Neuroscience Research Program, Faculty of Medicine and Health, School of Medical Sciences, The Kolling Institute, The University of Sydney, Sydney, New South Wales, Australia., Perez-Carpena P; Department of Otolaryngology, Hospital Universitario San Cecilio, Instituto de Investigación Biosanitaria, Ibs.GRANADA, Granada, Spain.; Otology and Neurotology Group CTS495, Instituto de Investigación Biosanitaria, ibs.GRANADA, Universidad de Granada, Granada, Spain.; Sensorineural Pathology Programme, Centro de Investigación Biomédica en Red en Enfermedades Raras, CIBERER, Madrid, Spain.; Division of Otolaryngology, Department of Surgery, Instituto de Investigación Biosanitaria, ibs.GRANADA, Universidad de Granada, Granada, Spain.
Source:
Headache [Headache] 2025 Apr; Vol. 65 (4), pp. 695-708. Date of Electronic Publication: 2025 Mar 13.
Publication Type:
Systematic Review; Journal Article; Meta-Analysis
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: United States NLM ID: 2985091R Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1526-4610 (Electronic) Linking ISSN: 00178748 NLM ISO Abbreviation: Headache Subsets: MEDLINE
Imprint Name(s):
Publication: <2008- > : Hoboken, NJ : Wiley
Original Publication: St. Louis : American Association for the Study of Headache
Original Publication: St. Louis : American Association for the Study of Headache
MeSH Terms:
References:
Lempert T, Olesen J, Furman J, et al. Vestibular migraine: Diagnostic criteria1. J Vestib Res. 2022;32(1):1‐6.
Formeister EJ, Rizk HG, Kohn MA, Sharon JD. The epidemiology of vestibular migraine: a population‐based survey study. Otol Neurotol. 2018;39(8):1037‐1044.
Dieterich M, Obermann M, Celebisoy N. Vestibular migraine: the most frequent entity of episodic vertigo. J Neurol. 2016;263(Suppl 1):S82‐S89.
Liu YF, Xu H. The intimate relationship between vestibular migraine and Meniere disease: a review of pathogenesis and presentation. Behav Neurol. 2016;2016:3182735.
Radtke A, Lempert T, Gresty MA, Brookes GB, Bronstein AM, Neuhauser H. Migraine and Ménière's disease: is there a link? Neurology. 2002;59(11):1700‐1704.
Neff BA, Staab JP, Eggers SD, et al. Auditory and vestibular symptoms and chronic subjective dizziness in patients with Ménière's disease, vestibular migraine, and Ménière's disease with concomitant vestibular migraine. Otol Neurotol. 2012;33(7):1235‐1244.
Cha YH, Kane MJ, Baloh RW. Familial clustering of migraine, episodic vertigo, and Ménière's disease. Otol Neurotol. 2008;29(1):93‐96.
Formeister EJ, Baum RT, Sharon JD. Supervised machine learning models for classifying common causes of dizziness. Am J Otolaryngol. 2022;43(3):103402.
Ahmadi SA, Vivar G, Navab N, et al. Modern machine‐learning can support diagnostic differentiation of central and peripheral acute vestibular disorders. J Neurol. 2020;267(Suppl 1):143‐152.
Hannigan IP, Rosengren SM, Bharathy GK, Prasad M, Welgampola MS, Watson SRD. Subjective and objective responses to caloric stimulation help separate vestibular migraine from other vestibular disorders. J Neurol. 2024;271(2):887‐898.
Wang C, Young AS, Raj C, et al. Machine learning models help differentiate between causes of recurrent spontaneous vertigo. J Neurol. 2024;271(6):3426‐3438.
Halmágyi GM, Akdal G, Welgampola MS, Wang C. Neurological update: neuro‐otology 2023. J Neurol. 2023;270(12):6170‐6192.
Flook M, Frejo L, Gallego‐Martinez A, et al. Differential proinflammatory signature in vestibular migraine and meniere disease. Front Immunol. 2019;10:1229.
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.
Sounderajah V, Ashrafian H, Rose S, et al. A quality assessment tool for artificial intelligence‐centered diagnostic test accuracy studies: QUADAS‐AI. Nat Med. 2021;27(10):1663‐1665.
Kim KW, Lee J, Choi SH, Huh J, Park SH. Systematic review and meta‐analysis of studies evaluating diagnostic test accuracy: a practical review for clinical researchers‐part I. General guidance and tips. Korean J Radiol. 2015;16(6):1175‐1187.
Reitsma JB, Glas AS, Rutjes AWS, Scholten RJPM, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol. 2005;58(10):982‐990. Accessed September 18, 2024. https://pubmed.ncbi.nlm.nih.gov/16168343/.
Shim SR, Kim SJ, Lee J. Diagnostic test accuracy: application and practice using R software. Epidemiol Health. 2019;41:e2019007.
Filippopulos FM, Strobl R, Belanovic B, et al. Validation of a comprehensive diagnostic algorithm for patients with acute vertigo and dizziness. Eur J Neurol. 2022;29(10):3092‐3101.
Feil K, Feuerecker R, Goldschagg N, et al. Predictive capability of an iPad‐based medical device (medx) for the diagnosis of vertigo and dizziness. Front Neurol. 2018;9:29.
Strobl R, Grözinger M, Zwergal A, Huppert D, Filippopulos F, Grill E. A set of eight key questions helps to classify common vestibular disorders—results from the DizzyReg patient registry. Front Neurol. 2021;12:670944.
Friedland DR, Tarima S, Erbe C, Miles A. Development of a statistical model for the prediction of common vestibular diagnoses. JAMA Otolaryngol Head Neck Surg. 2016;142(4):351‐356.
Luo J, Erbe C, Friedland DR. Unique clinical language patterns among expert vestibular providers can predict vestibular diagnoses. Otol Neurotol. 2018;39(9):1163‐1171.
Yu F, Wu P, Deng H, et al. A questionnaire‐based ensemble learning model to predict the diagnosis of vertigo: model development and validation study. J Med Internet Res. 2022;24(8):e34126.
Groezinger M, Huppert D, Strobl R, Grill E. Development and validation of a classification algorithm to diagnose and differentiate spontaneous episodic vertigo syndromes: results from the DizzyReg patient registry. J Neurol. 2020;267(Suppl 1):160‐167.
Vivar G, Strobl R, Grill E, Navab N, Zwergal A, Ahmadi SA. Using base‐ml to learn classification of common vestibular disorders on DizzyReg registry data. Front Neurol. 2021;12:681140.
Liu D, Guo Z, Wang J, et al. Development and validation of the predictive model for the differentiation between vestibular migraine and Meniere's disease. J Clin Med. 2022;11(16):4745.
Dong C, Wang Y, Zhang Q, Wang N. The methodology of dynamic uncertain causality graph for intelligent diagnosis of vertigo. Comput Methods Prog Biomed. 2014;113(1):162‐174.
Exarchos TP, Rigas G, Bibas A, et al. Mining balance disorders' data for the development of diagnostic decision support systems. Comput Biol Med. 2016;77:240‐248.
Mira E, Buizza A, Magenes G, Manfrin M, Schmid R. Expert systems as a diagnostic aid in otoneurology. ORL. 2010;52(2):96‐103.
Gavilán C, Gallego J, Gavilán J. ‘Carnisel’: an expert system for vestibular diagnosis. Acta Otolaryngol. 1990;110(3–4):161‐167.
Kentala E, Pyykkö I, Auramo Y, Laurikkala J, Juhola M. Otoneurological expert system for vertigo. Acta Otolaryngol. 1999;119(5):517‐521.
van de Berg R, Kingma H. History taking in non‐acute vestibular symptoms: a 4‐step approach. J Clin Med. 2021;10(24):5726.
Newman‐Toker DE, Edlow JA. TiTrATE: a novel approach to diagnosing acute dizziness and vertigo. Neurol Clin. 2015;33(3):577‐599.
Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Human Comput. 2023;14(7):8459‐8486.
van Buuren S. Flexible Imputation of Missing Data. 2nd ed. Chapman and Hall/CRC; 2018. 444 p.
Samek W, Montavon G, Vedaldi A, Hansen LK, Müller KR. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning [Internet]. Springer International Publishing; 2019. (Lecture Notes in Computer Science; vol. 11700). Accessed July 7, 2024. doi:10.1007/978-3-030-28954-6.
Varpa K, Joutsijoki H, Iltanen K, Juhola M. Applying one‐vs‐one and one‐vs‐all classifiers in k‐nearest neighbour method and support vector machines to an otoneurological multi‐class problem. Stud Health Technol Inform. 2011;169:579‐583.
Formeister EJ, Rizk HG, Kohn MA, Sharon JD. The epidemiology of vestibular migraine: a population‐based survey study. Otol Neurotol. 2018;39(8):1037‐1044.
Dieterich M, Obermann M, Celebisoy N. Vestibular migraine: the most frequent entity of episodic vertigo. J Neurol. 2016;263(Suppl 1):S82‐S89.
Liu YF, Xu H. The intimate relationship between vestibular migraine and Meniere disease: a review of pathogenesis and presentation. Behav Neurol. 2016;2016:3182735.
Radtke A, Lempert T, Gresty MA, Brookes GB, Bronstein AM, Neuhauser H. Migraine and Ménière's disease: is there a link? Neurology. 2002;59(11):1700‐1704.
Neff BA, Staab JP, Eggers SD, et al. Auditory and vestibular symptoms and chronic subjective dizziness in patients with Ménière's disease, vestibular migraine, and Ménière's disease with concomitant vestibular migraine. Otol Neurotol. 2012;33(7):1235‐1244.
Cha YH, Kane MJ, Baloh RW. Familial clustering of migraine, episodic vertigo, and Ménière's disease. Otol Neurotol. 2008;29(1):93‐96.
Formeister EJ, Baum RT, Sharon JD. Supervised machine learning models for classifying common causes of dizziness. Am J Otolaryngol. 2022;43(3):103402.
Ahmadi SA, Vivar G, Navab N, et al. Modern machine‐learning can support diagnostic differentiation of central and peripheral acute vestibular disorders. J Neurol. 2020;267(Suppl 1):143‐152.
Hannigan IP, Rosengren SM, Bharathy GK, Prasad M, Welgampola MS, Watson SRD. Subjective and objective responses to caloric stimulation help separate vestibular migraine from other vestibular disorders. J Neurol. 2024;271(2):887‐898.
Wang C, Young AS, Raj C, et al. Machine learning models help differentiate between causes of recurrent spontaneous vertigo. J Neurol. 2024;271(6):3426‐3438.
Halmágyi GM, Akdal G, Welgampola MS, Wang C. Neurological update: neuro‐otology 2023. J Neurol. 2023;270(12):6170‐6192.
Flook M, Frejo L, Gallego‐Martinez A, et al. Differential proinflammatory signature in vestibular migraine and meniere disease. Front Immunol. 2019;10:1229.
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.
Sounderajah V, Ashrafian H, Rose S, et al. A quality assessment tool for artificial intelligence‐centered diagnostic test accuracy studies: QUADAS‐AI. Nat Med. 2021;27(10):1663‐1665.
Kim KW, Lee J, Choi SH, Huh J, Park SH. Systematic review and meta‐analysis of studies evaluating diagnostic test accuracy: a practical review for clinical researchers‐part I. General guidance and tips. Korean J Radiol. 2015;16(6):1175‐1187.
Reitsma JB, Glas AS, Rutjes AWS, Scholten RJPM, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol. 2005;58(10):982‐990. Accessed September 18, 2024. https://pubmed.ncbi.nlm.nih.gov/16168343/.
Shim SR, Kim SJ, Lee J. Diagnostic test accuracy: application and practice using R software. Epidemiol Health. 2019;41:e2019007.
Filippopulos FM, Strobl R, Belanovic B, et al. Validation of a comprehensive diagnostic algorithm for patients with acute vertigo and dizziness. Eur J Neurol. 2022;29(10):3092‐3101.
Feil K, Feuerecker R, Goldschagg N, et al. Predictive capability of an iPad‐based medical device (medx) for the diagnosis of vertigo and dizziness. Front Neurol. 2018;9:29.
Strobl R, Grözinger M, Zwergal A, Huppert D, Filippopulos F, Grill E. A set of eight key questions helps to classify common vestibular disorders—results from the DizzyReg patient registry. Front Neurol. 2021;12:670944.
Friedland DR, Tarima S, Erbe C, Miles A. Development of a statistical model for the prediction of common vestibular diagnoses. JAMA Otolaryngol Head Neck Surg. 2016;142(4):351‐356.
Luo J, Erbe C, Friedland DR. Unique clinical language patterns among expert vestibular providers can predict vestibular diagnoses. Otol Neurotol. 2018;39(9):1163‐1171.
Yu F, Wu P, Deng H, et al. A questionnaire‐based ensemble learning model to predict the diagnosis of vertigo: model development and validation study. J Med Internet Res. 2022;24(8):e34126.
Groezinger M, Huppert D, Strobl R, Grill E. Development and validation of a classification algorithm to diagnose and differentiate spontaneous episodic vertigo syndromes: results from the DizzyReg patient registry. J Neurol. 2020;267(Suppl 1):160‐167.
Vivar G, Strobl R, Grill E, Navab N, Zwergal A, Ahmadi SA. Using base‐ml to learn classification of common vestibular disorders on DizzyReg registry data. Front Neurol. 2021;12:681140.
Liu D, Guo Z, Wang J, et al. Development and validation of the predictive model for the differentiation between vestibular migraine and Meniere's disease. J Clin Med. 2022;11(16):4745.
Dong C, Wang Y, Zhang Q, Wang N. The methodology of dynamic uncertain causality graph for intelligent diagnosis of vertigo. Comput Methods Prog Biomed. 2014;113(1):162‐174.
Exarchos TP, Rigas G, Bibas A, et al. Mining balance disorders' data for the development of diagnostic decision support systems. Comput Biol Med. 2016;77:240‐248.
Mira E, Buizza A, Magenes G, Manfrin M, Schmid R. Expert systems as a diagnostic aid in otoneurology. ORL. 2010;52(2):96‐103.
Gavilán C, Gallego J, Gavilán J. ‘Carnisel’: an expert system for vestibular diagnosis. Acta Otolaryngol. 1990;110(3–4):161‐167.
Kentala E, Pyykkö I, Auramo Y, Laurikkala J, Juhola M. Otoneurological expert system for vertigo. Acta Otolaryngol. 1999;119(5):517‐521.
van de Berg R, Kingma H. History taking in non‐acute vestibular symptoms: a 4‐step approach. J Clin Med. 2021;10(24):5726.
Newman‐Toker DE, Edlow JA. TiTrATE: a novel approach to diagnosing acute dizziness and vertigo. Neurol Clin. 2015;33(3):577‐599.
Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Human Comput. 2023;14(7):8459‐8486.
van Buuren S. Flexible Imputation of Missing Data. 2nd ed. Chapman and Hall/CRC; 2018. 444 p.
Samek W, Montavon G, Vedaldi A, Hansen LK, Müller KR. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning [Internet]. Springer International Publishing; 2019. (Lecture Notes in Computer Science; vol. 11700). Accessed July 7, 2024. doi:10.1007/978-3-030-28954-6.
Varpa K, Joutsijoki H, Iltanen K, Juhola M. Applying one‐vs‐one and one‐vs‐all classifiers in k‐nearest neighbour method and support vector machines to an otoneurological multi‐class problem. Stud Health Technol Inform. 2011;169:579‐583.
Grant Information:
PREDOC2021/00343 Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía; K7013_B3413 Faculty of Medicine and Health, University of Sydney; PI22/01838 Instituto de Salud Carlos III
Contributed Indexing:
Keywords: classification algorithms; differential diagnosis; machine learning; vestibular migraine
Entry Date(s):
Date Created: 20250313 Date Completed: 20250328 Latest Revision: 20250728
Update Code:
20260130
DOI:
10.1111/head.14924
PMID:
40079713
Database:
MEDLINE
*Further Information*
*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, I<sup>2</sup> = 96%), while the global specificity was 0.89 (95% CI 0.84-0.93, I<sup>2</sup> = 95%). The pooled diagnostic odds ratio was 48.15 (95% CI 17.64-131.43, I<sup>2</sup> = 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.
(© 2025 American Headache Society.)*