Researchers have shown that patients with benign vocal fold lesions and laryngeal cancer can be distinguished through acoustic features of the voice, especially the harmonic-to-noise ratio and its variation within speech. Now that the proof-of-principle has been established, this task can be fed into machine learning algorithms, to build AI applications that triage patients at risk for laryngeal cancer based on their voice.

Cancer of the voice box or larynx is an important public health burden. In 2021, there were an estimated 1.1 million cases of laryngeal cancer worldwide, and approximately 100,000 people died from it. Risk factors include smoking, alcohol abuse, and infection with human papillomavirus. The prognosis for laryngeal cancer ranges from 35% to 78% survival over five years when treated, depending on the tumor’s stage and its location within the voice box.
Catching cancer early is key for a patient’s prospects. At present, laryngeal cancers are diagnosed through video nasal endoscopy and biopsies – onerous, invasive procedures.

Researchers have shown in Frontiers in Digital Health that abnormalities of the vocal folds can be detected from the sound of the voice. These proof-of-principle results open the door for a new application of AI: namely, to recognize the early warning stages of laryngeal cancer from voice recordings.

Full article: https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1609811/full

According to the International Head and Neck Cancer Epidemiology

Consortium (INHANCE), a global research group that compiles data from multiple studies, people who drank four or more cups of coffee per day were 17% less likely to develop head and neck cancers.

Audio source Brutamerica