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European Archives of Oto-Rhino-Laryngology

Erschienen in:

13.12.2023 | Laryngology

Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review

verfasst von: Idit Tessler, Adi Primov-Fever, Shelly Soffer, Roi Anteby, Nir A. Gecel, Nir Livneh, Eran E. Alon, Eyal Zimlichman, Eyal Klang

Erschienen in: European Archives of Oto-Rhino-Laryngology | Ausgabe 2/2024

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Abstract

Objectives

With smartphones and wearable devices becoming ubiquitous, they offer an opportunity for large-scale voice sampling. This systematic review explores the application of deep learning models for the automated analysis of voice samples to detect vocal cord pathologies.

Methods

We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. We searched MEDLINE and Embase databases for original publications on deep learning applications for diagnosing vocal cord pathologies between 2002 and 2022. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2).

Results

Out of the 14 studies that met the inclusion criteria, data from a total of 3037 patients were analyzed. All studies were retrospective. Deep learning applications targeted Reinke's edema, nodules, polyps, cysts, unilateral cord paralysis, and vocal fold cancer detection. Most pathologies had detection accuracy above 90%. Thirteen studies (93%) exhibited a high risk of bias and concerns about applicability.

Conclusions

Technology holds promise for enhancing the screening and diagnosis of vocal cord pathologies. While current research is limited, the presented studies offer proof of concept for developing larger-scale solutions.

Cohen SM, Kim J, Roy N, Asche C, Courey M (2012) Direct health care costs of laryngeal diseases and disorders. Laryngoscope 122(7):1582–1588. https://doi.org/10.1002/lary.23189CrossRefPubMed

Martins RHG, do Amaral HA, Tavares ELM, Martins MG, Gonçalves TM, Dias NH (2016) Voice disorders: etiology and diagnosis. J Voice 30(6):761.e1-761.e9. https://doi.org/10.1016/j.jvoice.2015.09.017CrossRefPubMed

Cohen SM, Kim J, Roy N, Courey M (2015) Delayed otolaryngology referral for voice disorders increases health care costs. Am J Med 128(4):426.e11-426.e18. https://doi.org/10.1016/j.amjmed.2014.10.040CrossRefPubMed

Rejaibi E, Komaty A, Meriaudeau F, Agrebi S, Othmani A (2022) MFCC-based Recurrent Neural Network for automatic clinical depression recognition and assessment from speech. Biomed Signal Process Control 71:103107. https://doi.org/10.1016/j.bspc.2021.103107CrossRef

Hireš M, Gazda M, Drotár P, Pah ND, Motin MA, Kumar DK (2022) Convolutional neural network ensemble for Parkinson’s disease detection from voice recordings. Comput Biol Med 141:105021. https://doi.org/10.1016/j.compbiomed.2021.105021CrossRefPubMed

Sorin V, Barash Y, Konen E, Klang E (2020) Deep-learning natural language processing for oncological applications. Lancet Oncol 21(12):1553–1556. https://doi.org/10.1016/S1470-2045(20)30615-XCrossRefPubMed

Soffer S, Ben-Cohen A, Shimon O, Amitai MM, Greenspan H, Klang E (2019) Convolutional neural networks for radiologic images: a radiologist’s guide. Radiology 290(3):590–606. https://doi.org/10.1148/radiol.2018180547CrossRefPubMed

Schönweiler R, Hess M, Wübbelt P, Ptok M (2000) Novel approach to acoustical voice analysis using artificial neural networks. J Assoc Res Otolaryngol 1(4):270–282. https://doi.org/10.1007/s101620010020CrossRefPubMed

Linder R, Albers AE, Hess M, Pöppl SJ, Schönweiler R (2008) Artificial neural network-based classification to screen for dysphonia using psychoacoustic scaling of acoustic voice features. J Voice 22(2):155–163. https://doi.org/10.1016/j.jvoice.2006.09.003CrossRefPubMed

10.

Kim H, Jeon J, Han YJ et al (2020) Convolutional Neural Network classifies pathological voice change in laryngeal cancer with high accuracy. J Clin Med. https://doi.org/10.3390/jcm9113415CrossRefPubMedPubMedCentral

11.

Balamurali BT, Hee HI, Teoh OH et al (2020) Asthmatic versus healthy child classification based on cough and vocalised/ɑ:/sounds. J Acoust Soc Am 148(3):EL253. https://doi.org/10.1121/10.0001933CrossRef

12.

Fagherazzi G, Fischer A, Ismael M, Despotovic V (2021) Voice for health: the use of vocal biomarkers from research to clinical practice. Digit Biomark 5(1):78–88. https://doi.org/10.1159/000515346CrossRefPubMedPubMedCentral

13.

Whiting PF, Rutjes AWS, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529–536. https://doi.org/10.7326/0003-4819-155-8-201110180-00009CrossRefPubMed

14.

Munn Z, Moola S, Riitano D, Lisy K (2014) The development of a critical appraisal tool for use in systematic reviews addressing questions of prevalence. Int J Health Policy Manag 3(3):123–128. https://doi.org/10.15171/ijhpm.2014.71CrossRefPubMedPubMedCentral

15.

Luo W, Phung D, Tran T et al (2016) Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res 18(12):e323. https://doi.org/10.2196/jmir.5870CrossRefPubMedPubMedCentral

16.

Hu H-C, Chang S-Y, Wang C-H et al (2021) Deep learning application for vocal fold disease prediction through voice recognition: preliminary development study. J Med Internet Res 23(6):e25247. https://doi.org/10.2196/25247CrossRefPubMedPubMedCentral

17.

Davis S, Mermelstein P (1980) Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans Acoust 28(4):357–366. https://doi.org/10.1109/TASSP.1980.1163420CrossRef

18.

Francis CR, Nair VV, Radhika S (2016) A scale invariant technique for detection of voice disorders using Modified Mellin Transform. In: 2016 International Conference on Emerging Technological Trends (ICETT). IEEE; 1–6. https://doi.org/10.1109/ICETT.2016.7873650

19.

Carvalho RTS, Cavalcante CC, Cortez PC (2011) Wavelet transform and artificial neural networks applied to voice disorders identification. In: 2011 Third World Congress on Nature and Biologically Inspired Computing. IEEE; 371–376.https://doi.org/10.1109/NaBIC.2011.6089256

20.

LA Forero M, Kohler M, Vellasco MMBR, Cataldo E (2016) Analysis and classification of voice pathologies using glottal signal parameters. J Voice 30(5):549–556. https://doi.org/10.1016/j.jvoice.2015.06.010CrossRef

21.

Dias D, Paulo Silva Cunha J (2018) Wearable health devices-vital sign monitoring, systems and technologies. Sensors. https://doi.org/10.3390/s18082414CrossRefPubMedPubMedCentral

22.

Sheikh M, Qassem M, Kyriacou PA (2021) Wearable, environmental, and smartphone-based passive sensing for mental health monitoring. Front Digit Health 3:662811. https://doi.org/10.3389/fdgth.2021.662811CrossRefPubMedPubMedCentral

23.

Milling M, Pokorny FB, Bartl-Pokorny KD, Schuller BW (2022) Is speech the new blood? Recent progress in AI-based disease detection from audio in a nutshell. Front Digit Health 4:886615. https://doi.org/10.3389/fdgth.2022.886615CrossRefPubMedPubMedCentral

24.

Zhang Z (2020) Estimation of vocal fold physiology from voice acoustics using machine learning. J Acoust Soc Am 147(3):EL264. https://doi.org/10.1121/10.0000927CrossRefPubMedPubMedCentral

25.

Wang K, Lu X, Zhou H et al (2019) Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut 68(4):729–741. https://doi.org/10.1136/gutjnl-2018-316204CrossRefPubMed

26.

Anteby R, Horesh N, Soffer S et al (2021) Deep learning visual analysis in laparoscopic surgery: a systematic review and diagnostic test accuracy meta-analysis. Surg Endosc 35(4):1521–1533. https://doi.org/10.1007/s00464-020-08168-1CrossRefPubMed

Titel: Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review
verfasst von: Idit Tessler
Adi Primov-Fever
Shelly Soffer
Roi Anteby
Nir A. Gecel
Nir Livneh
Eran E. Alon
Eyal Zimlichman
Eyal Klang
Publikationsdatum: 13.12.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: European Archives of Oto-Rhino-Laryngology / Ausgabe 2/2024
Print ISSN: 0937-4477
Elektronische ISSN: 1434-4726
DOI: https://doi.org/10.1007/s00405-023-08362-6

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Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review