Researchers have achieved impressive results using machine learning to detect dystonias and laryngeal masses that often manifest as vocal abnormalities, with some teams reporting sensitivities as high as 100% (J Voice. 2019;33:947.e11-947.e33). The problem has been replicating those results in real-world clinical practice.
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November 2020In late September, investigators at Massachusetts Eye and Ear in Boston took a major step in the effort to turn machine learning into a diagnostic tool for the masses. This unique artificial intelligence (AI)-driven diagnostic tool has uncovered, for the first time, microstructural neural networks in the magnetic resonance imaging (MRI) scans of patients with laryngeal dystonia that are proving to be highly reliable biomarkers for the condition (PNAS [Published online September 28, 2020]. doi: 10.1073/pnas.2009165117).
Such a diagnostic tool is sorely needed. Like many vocal abnormalities, laryngeal dystonia (also referred to as spasmodic dysphonia) often is mistaken for other voice disorders. As a result, it can take an average of up to five years after symptom onset for patients to be correctly diagnosed (J Voice. 2015;29:592-594). With the new AI-powered diagnostic tool, in contrast, a definitive diagnosis can be obtained in less than 1 second after scanning a patient’s brain, Kristina Simonyan, MD, PhD, the director of laryngology research at Massachusetts Eye and Ear, told ENTtoday.
In the study, Dr. Simonyan and her colleagues analyzed brain MRIs obtained from 392 patients with three of the most common types of focal dystonia: laryngeal dystonia, cervical dystonia, and blepharospasm. They then compared those MRIs with imaging obtained from 220 healthy individuals and found that the platform diagnosed dystonia with 98.8% accuracy.
Ready for the Clinic
Plans are in place to bring the diagnostic tool out of the lab and into clinical practice. “We designed this platform with exactly that in mind—to quickly get this tool into the hands of practitioners,” Dr. Simonyan said.
To that end, when the team tested the algorithm, they found that in addition to working with a 3.0 Tesla high-resolution MRI, it also was comparably effective when used with a 1.5 Tesla MRI, “a more mainstream scanner available in most clinics around the country, as well as worldwide, so there shouldn’t be a major problem with its widespread use,” Dr. Simonyan said. “One can use conventional MRIs along with the usual types of raw structural images that clinicians order for these patients.”
Given how well the algorithm performed, one might assume that high-powered computers and software are needed to make the platform work. But that isn’t the case, noted co-investigator and first author of the study Davide Valeriani, PhD, a postdoctoral fellow in Dr. Simonyan’s Dystonia and Speech Motor Control Laboratory at Massachusetts Eye and Ear. He explained that the system runs on an easily accessible AI-based deep learning platform called DystoniaNet.
“All you need is a patient’s MRI and an internet connection,” Dr. Valeriani said. “This is a cloud-based platform, and it doesn’t even require installation—it’s truly plug-and-play. Already, we’re getting lots of interest from otolaryngologists who, even as specialists, often struggle to nail down a laryngeal dystonia diagnosis using conventional means.”
Dr. Simonyan said that excitement is understandable, given how few practitioners are trained to detect the subtle and often confusing signs of the voice disorder. “Less than 6% of speech language pathologists actually work in the clinical settings like primary care, where these patients are most likely to be seen,” she explained. “And even if they do make it to an otolaryngologist’s office, there, too, is a knowledge and training gap to overcome: Less than 2% of otolaryngologists are considered to be experts in neurologic laryngeal disorders.”
That neurological basis was the key that led the Mass Eye and Ear researchers to their breakthrough. “Even though laryngeal dystonia manifests as a voice disorder, and it would therefore be reasonable to assume it’s caused at least in part by structural abnormalities affecting the vocal cords, that’s typically not the case,” Dr. Simonyan said. “Rather, it’s caused by a neurological condition that affects speech production. If not for that, these patients’ physical findings appear quite normal in terms of function and anatomy.”
Dr. Valeriani stressed, however, that anatomy isn’t irrelevant when using the new diagnostic platform. “We need the structural brain MRI to rule out other neurological conditions in the current process of diagnosing dystonia,” he said. “But the real power of our system is using structural MRI to make the actual diagnosis, thanks to the machine/deep-learning platform’s ability to see microstructural changes in those MRI scans that simply cannot be seen with the human eye.”
Lest any otolaryngologist reads that quote and worries about being automated out of a job, Dr. Simonyan offered a very large caveat: “This is an objective measure of the disease rather than an AI platform that’s going to replace clinicians, otolaryngologists, or neurologists,” she said. “Their expertise cannot be replaced, but their clinical knowledge can be augmented by this tool—and frankly it needs to be, based on the delayed time to definitive diagnosis and treatment that’s held true for so long.”