Acoustic signatures of organic lesions and the role of artificial intelligence in voice disorder diagnostics
The integration of acoustic analysis with machine learning significantly enhances the diagnostic accuracy for vocal cord lesions, particularly in differentiating between benign and malignant cases. These findings underscore the potential of artificial intelligence (AI)-assisted voice analysis as a non-invasive tool for early detection and clinical decision-making. Further validation in larger cohorts is necessary to refine predictive algorithms for broader clinical application.
Authors
- Yulin Zhou (ORCID: https://orcid.org/0000-0002-4241-0214)
- Keyi Ma (ORCID: https://orcid.org/0000-0002-3834-3928)
- Xiaoli Peng (ORCID: https://orcid.org/0000-0002-3994-862X)
- Yi Wang (ORCID: https://orcid.org/0000-0001-5721-776X)
- Lan Chen (ORCID: https://orcid.org/0000-0003-4520-5109)
- Tiecheng Zhang (ORCID: https://orcid.org/0000-0002-9866-1258)
- Fan Xu (ORCID: https://orcid.org/0000-0001-9984-2854)
Institutions
- China National Nuclear Corporation (CN)
- Chengdu Medical College (CN)
- First Affiliated Hospital of Chengdu Medical College (CN)
- Chengdu Second People's Hospital (CN)
- Science and Technology Department of Sichuan Province (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251376264
- Primary Topic
- Voice and Speech Disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00