The impact of sound on early detection of laryngeal cancer

This study introduces a scientifically grounded, ISO-compliant framework for early and non-invasive detection of laryngeal cancer. By integrating acoustic voice analysis with genetic biomarker evaluation and adhering to international ISO standards, we propose a multi-modal diagnostic model with strong clinical potential. Voice samples were collected under standardized conditions, and genetic data were extracted from saliva. Machine learning algorithms, validated according to ISO/IEC quality protocols, were employed to analyze the combined dataset, achieving high diagnostic accuracy.