Trained AI can detect larynx cancer by listening to voice
Men with cancer of the larynx, or voice box, have distinct differences in their voices that could be detected with trained artificial intelligence, researchers reported Tuesday in the journal Frontiers in Digital Health.
These differences are caused by potentially cancerous lesions that have cropped up in a person's vocal folds -- the two bands of muscle tissue in the larynx that produce sound, also known as vocal cords.
"We could use vocal biomarkers to distinguish voices from patients with vocal fold lesions from those without such lesions," lead researcher Dr. Phillip Jenkins, a postdoctoral fellow in clinical informatics at Oregon Health & Science University in Portland, said in a news release.
Catching voice box cancer early can be a matter of life or death.
There were an estimated 1.1 million cases of laryngeal cancer worldwide in 2021, and about 100,000 people died from it, researchers said in background notes. Risk factors include smoking, drinking and HPV infection.
A person's odds of five-year survival can be as high as 78% if their throat cancer is caught at an early stage, or as low as 35% if it's caught late, researchers said.
For the study, researchers analyzed more than 12,500 voice recordings from 306 people across North America. These included a handful of people with either laryngeal cancer, benign vocal cord lesions or other vocal disorders.
Researchers discovered that the voices of men with laryngeal cancer exhibited marked differences in harmonic-to-noise ratio, which judges the amount of noise in a person's speech.
Men with laryngeal cancer also showed differences in the pitch of their voices, results show.
The team concluded that harmonic-to-noise ratio in particular might be used to track vocal cord lesions and potentially detect voice box cancer at an early stage, at least in men.
They weren't able to detect any differences among women with laryngeal cancer, but are hopeful a larger dataset might reveal such differences.
The next step will be to feed the AI more data and test its effectiveness with patients in clinical settings, researchers said.
"To move from this study to an AI tool that recognizes vocal fold lesions, we would train models using an even larger dataset of voice recordings, labeled by professionals," Jenkins said. Then, the system will need to be tested to make sure it works equally well for both women and men.
"Voice-based health tools are already being piloted," Jenkins added. "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."
More information
The American Cancer Society has more on throat cancers.
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A simple voice recording could one day help doctors spot early signs of throat cancer, according to new research. In a study published in Frontiers in Digital Health, scientists found that artificial intelligence (AI) could potentially detect abnormal growths on the vocal cords, from benign nodules to early-stage laryngeal cancer, by analysing short voice recordings. The findings could support efforts to find an easier, faster way to diagnose cancerous lesions on the vocal cords, also known as folds. 'With this dataset we could use vocal biomarkers to distinguish voices from patients with vocal fold lesions from those without such lesions,' said Phillip Jenkins, the study's lead author and a postdoctoral researcher in clinical informatics at Oregon Health and Science University in the United States. Related Experimental vaccine to fight cancer prompts immune response for some patients in small trial Why early detection of throat cancer matters Cancer of the voice box, or larynx, affects more than a million people worldwide and kills roughly 100,000 every year. It is the 20th most common cancer in the world. Smoking, alcohol use, and certain strains of HPV (human papillomavirus) are key risk factors, and survival rates vary from around 35 per cent to 90 per cent depending on how early the disease is diagnosed, according to Cancer Research UK. One of the most common warning signs for laryngeal cancer is hoarseness or changes in the voice that last more than three weeks. Other symptoms include a persistent sore throat or cough, difficulty or pain when swallowing, a lump in the neck or throat, and ear pain. Early detection of laryngeal cancer is crucial because it significantly improves survival rates and treatment outcomes. Related AI battled doctors in a live showdown to diagnose patients. Who came out on top? Yet current diagnostic methods, including nasal endoscopies and biopsies, are invasive, uncomfortable, and often slow, requiring specialist equipment and expertise that many patients struggle to access quickly. Developing a simple tool to flag early signs of vocal fold abnormalities through a quick voice recording could transform how throat cancer is detected – making it faster, more affordable and accessible to a wider population. The next steps for AI-driven diagnosis The research team examined about 12,500 voice recordings from 306 people across North America. They looked for subtle acoustic patterns, such as changes in pitch, loudness, and harmonic clarity. The team identified clear differences for men in the harmonic-to-noise ratio and pitch between those with healthy voices, benign lesions, and cancer. No significant patterns were found in women, but the researchers say this may be due to the smaller dataset. Related New AI tool is better than doctors at diagnosing complicated medical issues, Microsoft says Jenkins said that the results indicate large datasets "could soon help make our voice a practical biomarker for cancer risk in clinical care'. The next step is to train AI models on larger, professionally labelled datasets and test them in clinical settings. The team would also need to test the system to make sure it works well for both men and women, he said. 'Voice-based health tools are already being piloted," Jenkins said. "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".
