ArticleScientific reports2024
Classification of laryngeal diseases including laryngeal cancer, benign mucosal disease, and vocal cord paralysis by artificial intelligence using voice analysis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Early Detection of Glottic Cancer Through Machine Learning and Acoustic Voice Analysis: A Systematic Review.Medical sciences (Basel, Switzerland) · 2026Pooled it
- Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Application of artificial intelligence in early warning, diagnosis and precise treatment of head and neck tumors (Review).Molecular and clinical oncology · 2026Review
- Diagnostic Accuracy of Artificial Intelligence in Laryngeal Disorders: An Integrative Review.Journal of personalized medicine · 2026Review
- Hybrid deep learning model for multimodal vocal and lung signal analysis in health monitoring.Scientific reports · 2026Article
- CKAD-Net: class-guided key-feature aggregation and adaptive decision network for vocal cord lesion prediction.Scientific reports · 2026Article
- Research on automatic assessment of the severity of unilateral vocal cord paralysis based on Mel-spectrogram and convolutional neural networks.Biomedical engineering online · 2025Article
- Artificial Intelligence in Medicine: A Specialty-Level Overview of Emerging AI Trends.JSLS : Journal of the Society of Laparoendoscopic SurgeonsReview
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Voice change is often the first sign of laryngeal cancer, leading to diagnosis through hospital laryngoscopy. Screening for laryngeal cancer solely based on voice could enhance early detection. However, identifying voice indicators specific to laryngeal cancer is challenging, especially when differentiating it from other laryngeal ailments. This study presents an artificial intelligence model designed to distinguish between healthy voices, laryngeal cancer voices, and those of the other laryngeal conditions. We gathered voice samples of individuals with laryngeal cancer, vocal cord paralysis, benign mucosal diseases, and healthy participants. Comprehensive testing was conducted to determine the best mel-frequency cepstral coefficient conversion and machine learning techniques, with results analyzed in-depth. In our tests, laryngeal diseases distinguishing from healthy voices achieved an accuracy of 0.85-0.97. However, when multiclass classification, accuracy ranged from 0.75 to 0.83. These findings highlight the challenges of artificial intelligence-driven voice-based diagnosis due to overlaps with benign conditions but also underscore its potential.
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Registered trials
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