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Published: 2024-11-11
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DOI: https://doi.org/10.52852/tcncyh.v183i10.2587
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Vol. 183 No. 10 (2024)
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Anh, Đỗ T., Takakura, H., Asai, . M., Ueda, N., & Shojaku, H. (2024). Research on artificial intelligence (AI) and equilibrium function tests in the diagnosis of vestibular disease. Journal of Medical Research, 183(10), 1-10. https://doi.org/10.52852/tcncyh.v183i10.2587

Research on artificial intelligence (AI) and equilibrium function tests in the diagnosis of vestibular disease

Do Tram Anh, Hiromasa Takakura, Masatsugu Asai, Naoko Ueda, Hideo Shojaku

Main Article Content

Abstract

Peripheral vestibular (PV) disease is a common cause of dizziness. However, an accurate diagnosis of this condition requires significant time for clinical examination, comprehensive assessment, and differentiation from non-peripheral vestibular diseases (non-PV). Non-PV diseases encompass a wide range of conditions, with hemodynamic orthostatic dizziness/vertigo (HO) being the most prevalent in our data. This study aims to evaluate the effectiveness of applying machine learning which is a branch of artificial intelligence (AI) to diagnose HO and classify peripheral vestibular (PV) and non-peripheral vestibular (non-PV) categories. Multi-class classification models were tested on a dataset of 1,009 patients (497 PV, 157 HO, and 355 non-PV) and achieved an overall accuracy of 72%, with F1 scores of 0.78 for PV, 0.64 for non-PV, and 0.71 for HO. Our results show that AI can become a useful tool in clinical practice, permitting time saving and improve the accuracy and efficiency of disease diagnosis.

Article Details

Keywords

Machine learning, neuro-otological examinations, peripheral vestibular disease, hemodynamic orthostatic dizziness/vertigo

References

1. Labuguen RH. Initial evaluation of vertigo [published correction appears in Am Fam Physician. 2006 May 15;73(10):1704]. Am Fam Physician. 2006; 73(2): 244-251.
2. Strupp M, Feil K, Zwergal A. Diagnose und Differenzialdiagnose von peripheren und zentralen Schwindelsyndromen [Diagnosis and Differential Diagnosis of Peripheral and Central Vestibular Disorders]. Laryngorhinootologie. 2021; 100(3): 176-183. doi:10.1055/a-1057-3239.
3. Mayo RC, Leung J. Artificial intelligence and deep learning - Radiology’s next frontier?. Clin Imaging. 2018; 49: 87-88. doi:10.1016/j.clinimag.2017.11.007.
4. Egert M, Steward JE, Sundaram CP. Machine Learning and Artificial Intelligence in Surgical Fields. Indian J Surg Oncol. 2020; 11(4): 573-577. doi:10.1007/s13193-020-01166-8.
5. 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. doi:10.1007/s00415-020-09931-z.
6. Bisdorff A, Von Brevern M, Lempert T, Newman-Toker DE. Classification of vestibular symptoms: towards an international classification of vestibular disorders. J Vestib Res. 2009; 19(1-2): 1-13. doi:10.3233/VES-2009-0343.
7. Japan Society for Equilibrium Research. https:// www. memai. jp/ guide/.
8. Anh DT, Takakura H, Asai M, Ueda N, Shojaku H. Application of machine learning in the diagnosis of vestibular disease. Sci Rep. 2022; 12(1): 20805. Published 2022 Dec 2. doi:10.1038/s41598-022-24979-9.
9. Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Intern Med. 2018; 284(6): 603-619. doi:10.1111/joim.12822.
10. F. Pedregosa, G. Varoquaux, A. Gramfort, et al. Journal of Machine Learning Research. 2011; 12: 2825–2830.
11. Genuer, R., Poggi, J. M. & Tuleau-Malot, C. Variable selection using random forests. Pattern Recognit. Lett. 2010; 31: 2225–2236.
12. Masankaran, L., Viyanon, W. & Mahasittiwat, V. Classification of benign paroxysmal positioning vertigo types from Dizziness Handicap Inventory using machine learning techniques. International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS. 2018; 209–214.
13. Priesol AJ, Cao M, Brodley CE, Lewis RF. Clinical vestibular testing assessed with machine-learning algorithms. JAMA Otolaryngol Head Neck Surg. 2015; 141(4): 364-372. doi:10.1001/jamaoto.2014.3519.