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

  • Laryngology
  • Published:
European Archives of Oto-Rhino-Laryngology Aims and scope Submit manuscript

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.

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Data availability

The data that support the findings of this study are available on request from the corresponding author.

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Acknowledgements

Dr. Idit Tessler thanks to Dr. Orna Berry, Ph.D.; Technical Director office of the CTO, Google Cloud, for her constructive advice.

Funding

None.

Author information

Authors and Affiliations

  1. Department of Otolaryngology Head and Neck Surgery, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel

    Idit Tessler, Adi Primov-Fever, Nir Livneh & Eran E. Alon

  2. Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Idit Tessler, Adi Primov-Fever, Roi Anteby, Nir A. Gecel, Nir Livneh, Eran E. Alon, Eyal Zimlichman & Eyal Klang

  3. Department of Surgery and Transplantation B, Chaim Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel

    Roi Anteby

  4. Internal Medicine B, Assuta Medical Center, Ashdod, Israel

    Shelly Soffer

  5. Ben-Gurion University of the Negev, Be’er Sheva, Israel

    Shelly Soffer

  6. Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel

    Eyal Klang

  7. ARC Innovation Center, Sheba Medical Center, Tel-Hashomer, Israel

    Idit Tessler, Eyal Zimlichman & Eyal Klang

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  1. Idit Tessler
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  2. Adi Primov-Fever
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Correspondence to Idit Tessler.

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Conflict of interest

All authors declare not conflict of interest in regarding to this article.

Ethical approval

This systematic review did not involve accessing or analyzing patients-identifiable data, hence did not require ethical approval.

Meeting information

October 2022; The 16th meeting of the International Association of Phonosurgery; Oral presentation.

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Tessler, I., Primov-Fever, A., Soffer, S. et al. Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review. Eur Arch Otorhinolaryngol 281, 863–871 (2024). https://doi.org/10.1007/s00405-023-08362-6

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  • DOI: https://doi.org/10.1007/s00405-023-08362-6

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