Researchers have shown that AI can detect early laryngeal cancer and related lesions from voice recordings, offering a simple, non-invasive screening option
A new AI tool may be able to detect early laryngeal cancer based solely on a person’s voice. By analysing subtle changes in speech, the technology could provide a non-invasive, convenient way to identify the disease earlier, potentially improving outcomes and making screening more accessible.
Recognising the early warning signs of laryngeal cancer
Laryngeal cancer is a global health burden, and in 2021, there were an estimated 1.1 million cases of this cancer, 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 tumour’s stage.
It is vital to catch cancer as early as possible. Laryngeal cancer is currently diagnosed through video nasal endoscopy and biopsies, two invasive procedures. New findings published in Frontiers in Digital Health have revealed that abnormalities of the vocal folds can be detected from the sound of the voice. Using AI, the researchers applied it to recognise 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. Jenkins and his colleagues are members of the ‘Bridge2AI-Voice’ project within the US National Institute of Health’s ‘Bridge to Artificial Intelligence‘ (Bridge2AI) consortium, a nationwide endeavour to apply AI to complex biomedical challenges.
AI voice analysis may soon help detect vocal fold lesions in clinical care
The team analysed variations in tone, pitch, volume, and clarity within the first version of the public Bridge2AI-Voice dataset, with 12,523 voice recordings of 306 participants from across North America.
A minority were from patients with known laryngeal cancer, benign vocal fold lesions, or two other conditions of the voice box: spasmodic dysphonia and unilateral vocal fold paralysis.
The researchers analysed differences in several acoustic features of the voice: for example, the mean fundamental frequency (pitch); jitter, variation in pitch within speech; shimmer, variation of the amplitude; and the harmonic-to-noise ratio, a measure of the relation between harmonic and noise components of speech.
The team found marked differences in the harmonic-to-noise ratio and fundamental frequency between men without any voice disorder, men with benign vocal fold lesions, and men with laryngeal cancer. They found no informative acoustic features among women, but it is possible that a larger dataset would reveal such differences.
The authors concluded that especially variation in the harmonic-to-noise ratio can be helpful to monitor the clinical evolution of vocal fold lesions, and to detect laryngeal cancer at an early stage, at least in men.
“Our results suggest that ethically sourced, large, multi‑institutional datasets like Bridge2AI‑Voice could soon help make our voice a practical biomarker for cancer risk in clinical care,“ said Jenkins.
“To move from this study to an AI tool that recognises vocal fold lesions, we would train models using an even larger dataset of voice recordings, labelled by professionals. We then need to test the system to make sure it works equally well for women and men,“ said Jenkins.
“Voice-based health tools are already being piloted. Building on our findings, I estimate that with larger datasets and clinical validation, similar tools to detect vocal fold lesions might enter pilot testing in the next couple of years,“ predicted Jenkins.
