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 to a patient’s prospects. At present, laryngeal cancers are diagnosed through video nasal endoscopy and biopsies—onerous, invasive procedures. Getting to a specialist who can perform these procedures can take time, causing delays in diagnosis.

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.

“Here we show that with this dataset we could use vocal biomarkers to distinguish voices from patients with vocal fold lesions from those without such lesions,” said Dr. Phillip Jenkins, a postdoctoral fellow in clinical informatics at Oregon Health & Science University, and the study’s corresponding author.