Evidence map›Paper›PMID 38091100›Full record

SynthesisEuropean archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery2024

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

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

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Deep Learning in Otolaryngology: A Narrative Review.JAMA otolaryngology-- head & neck surgery · 2026
    Review
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Idit TesslerDepartment of Otolaryngology Head and Neck Surgery, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel. idit.tessler@gmail.com.ORCID http://orcid.org/0000-0002-3297-7022
Adi Primov-FeverDepartment of Otolaryngology Head and Neck Surgery, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel.
Shelly SofferInternal Medicine B, Assuta Medical Center, Ashdod, Israel.
Roi AntebyFaculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Nir A GecelFaculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Nir LivnehDepartment of Otolaryngology Head and Neck Surgery, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel.
Eran E AlonDepartment of Otolaryngology Head and Neck Surgery, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel.
Eyal ZimlichmanFaculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Eyal KlangFaculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWith 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.

methodsWe 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).

resultsOut 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.

conclusionsTechnology 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.

Indexed as

Deep LearningLaryngeal EdemaVocal Cord ParalysisHumansRetrospective StudiesVocal CordsConvolutional neural networkDeep learningNeural networkRecurrent neural networkVocal cord pathologiesVoice

Identifiers

PMID38091100

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.