The Use of Deep Learning Software in the Detection of Voice Disorders: A Systematic Review
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Objective: To summarize the use of deep learning in the detection of voice disorders using acoustic and laryngoscopic input, compare specific neural networks in terms of accuracy, and assess their effectiveness compared to expert clinical visual examination. Data Sources: Embase, MEDLINE, and Cochrane Central. Review Methods: Databases were screened through November 11, 2023 for relevant studies. The inclusion criteria required studies to utilize a specified deep learning method, use laryngoscopy or acoustic input, and measure accuracy of binary classification between healthy patients and those with voice disorders. Results: Thirty‐four studies met the inclusion criteria, with 18 focusing on voice analysis, 15 on imaging analysis, and 1 both. Across the 18 acoustic studies, 21 programs were used for identification of organic and functional voice disorders. These technologies included 10 convolutional neural networks (CNNs), 6 multilayer perceptrons (MLPs), and 5 other neural networks. The binary classification systems yielded a mean accuracy of 89.0% overall, including 93.7% for MLP programs and 84.5% for CNNs. Among the 15 imaging analysis studies, a total of 23 programs were utilized, resulting in a mean accuracy of 91.3%. Specifically, the twenty CNNs achieved a mean accuracy of 92.6% compared to 83.0% for the 3 MLPs. Conclusion: Deep learning models were shown to be highly accurate in the detection of voice pathology, with CNNs most effective for assessing laryngoscopy images and MLPs most effective for assessing acoustic input. While deep learning methods outperformed expert clinical exam in limited comparisons, further studies integrating external validation are necessary.
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Diagnostic performance ranging from 90% to 99% has been reported across acoustic and laryngeal image-based tasks.
“Diagnostic performance ranging from 90% to 99% has been reported across acoustic and laryngeal image-based tasks [26,30,31,32,33,34].”
From Diagnostic Accuracy of Artificial Intelligence in Laryngeal Disorders: An Integrative Review (2026) · cites this paper as [33]
CNNs have been widely applied to vocal spectrograms and laryngoscopic images, while recurrent and bidirectional architectures capture temporal dynamics of pathological phonation.
“Convolutional neural networks (CNNs) have been widely applied to vocal spectrograms and laryngoscopic images [24,33], while recurrent and bidirectional architectures capture temporal dynamics of pathological phonation [16,56,57].”
From Diagnostic Accuracy of Artificial Intelligence in Laryngeal Disorders: An Integrative Review (2026) · cites this paper as [33]
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A multimodal approach for deep-learning classification of vocal fold pathologies in stroboscopy
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Diagnostic Accuracy of Artificial Intelligence in Laryngeal Disorders: An Integrative Review
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“Reliance on internal cross-validation without external testing remains the principal analysis bias [13,33].”
Influential
- Authors
- Barlow, J., Sragi, Z., Rivera‐Rivera, G., Al‐Awady, A., Daşdöğen, Ü., Courey, M. S., & Kirke, D. N.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Barlow, Joshua
- Venue
- Otolaryngology–Head and Neck Surgery
- Publisher
- Wiley
- Published
- 2024-01-03
- Volume
- 170
- Issue
- 6
- Pages
- 1531-1543
- ISSN
- 0161-6439 (ISSN-L)
- 0194-5998
- 1097-6817
- OpenAlex
- W4390543736
- Cited by
- 25
- References
- 62
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 9
- FWCI
- 1.42
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Otolaryngology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- voice disorders, deep learning, acoustic voice analysis, convolutional neural networks, multilayer perceptron, automated diagnosis, diagnostic accuracy
- MeSH terms
- Deep Learning, Humans, Laryngoscopy, Software, Voice Disorders, Neural Networks, Computer
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