NIH Funding Power
The National Institutes of Health (NIH) sees the value of machine learning in otolaryngology, as evidenced by a grant it recently bestowed on Andrés Bur, MD, an assistant professor and director of robotics and minimally invasive head and neck surgery at The University of Kansas (KU) Medical Center in Kansas City. The focus of the grant is to support the use of machine learning to help detect and classify structural laryngeal lesions based on visual images obtained during endoscopy.
Explore This IssueNovember 2020
On first glance, Dr. Bur noted, the grant work isn’t specifically focused on speech pathology. “But it’s still very related,” he noted. “Laryngeal lesions are often detected because of dysphonia or some other change in the voice. Patients who live in rural communities, which is a big issue in this part of the country, may not have ready access to an otolaryngologist to identify and better characterize the laryngeal lesion and its related voice disorders. That’s where machine learning comes in. The long-term goal of our work is to increase laryngology care access and improve early detection of laryngeal cancers using neural networks.”
We’re very hopeful our approach will become that elusive, accurate vocal test with an actionable snapshot of the acoustic signature that can be followed very accurately over time, much like an EKG or a pulmonary function test. —Tanya Meyer, MD
The link between lesions and voice is partly why Dr. Bur is collaborating with Shannon Kraft, MD, an associate professor in the KU department of otolaryngology–head and neck surgery who specializes in voice disorders, and Guanghui Wang, PhD, an expert in computer vision and assistant professor of electrical engineering and computer science at KU.
Dr. Bur said his team is employing a convolutional neural network to process large sets of images of the larynx obtained from patients treated at his institution. “We’re basically trying to ‘teach’ the network to process the images and determine whether there’s a lesion present and, if there is a lesion, to classify it. And then, based on the results, ideally, we’d like to use this tool to recommend the next diagnostic and/or treatment steps that may be needed.
“At the end of the day, my primary focus is to care for patients with laryngeal cancers,” Dr. Bur continued. “And, unfortunately, I too often see these lesions not being diagnosed promptly. In the case of cancer, that can have catastrophic consequences. We’re using a visual-based approach with machine learning to analyze large databases of images of the larynx obtained from in-office exams to see if we can develop algorithms to facilitate early diagnosis.”