Author:
Kim Hyun-Bum,Song Jaemin,Park Seho,Lee Yong Oh
Abstract
AbstractVoice 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.
Publisher
Springer Science and Business Media LLC
Reference37 articles.
1. National Cancer Institute. SEER Cancer Stat Facts: Laryngeal Cancer (2023).
2. Law, A. B. & Schmitt, N. C. Laryngeal anatomy, molecular biology, cause, and risk factors for laryngeal cancer. Otolaryngol. Clin. N. Am. 56, 197–203 (2023).
3. The incidence of laryngeal cancer in korea. https://kosis.kr/statHtml/statHtml.do?orgId=117 &tblId=DT_117N_A00025 &conn_path=I2 (2023).
4. Jenkins, J. S. The lost voice: A history of the castrato: St george’s hospital medical school, London, UK. J. Pediatr. Endocrinol. Metab. 13, 1503–1508 (2000).
5. Born, H. & Rameau, A. Hoarseness. Med. Clin. 105, 917–938 (2021).