Researchers at Harvard Medical School and Massachusetts Eye and Ear have developed a unique diagnostic tool that can detect dystonia from MRI scans—the first technology of its kind to provide an objective diagnosis of the disorder. Dystonia is a potentially disabling neurological condition that causes involuntary muscle contractions, leading to abnormal movements and postures. It is often misdiagnosed and can take up to 10 years to get a correct diagnosis.
In a new study published September 28 in PNAS, researchers developed an AI-based deep learning platform, DystoniaNet, to compare brain MRIs of 612 people, including 392 patients with three different forms of isolated focal dystonia and 220 healthy individuals. The platform diagnosed dystonia with 98.8 percent accuracy. During the process, the researchers identified a new microstructural neural network biological marker of dystonia. With further testing and validation, they believe DystoniaNet can be easily integrated into clinical decision-making.
“There is currently no biomarker of dystonia and no gold-standard test for its diagnosis. Because of this, many patients have to undergo unnecessary procedures and see different specialists until other diseases are ruled out and the diagnosis of dystonia is established,” said senior study author Kristina Simonyan, HMS associate professor of otolaryngology head and neck surgery and director of laryngology research at Mass Eye and Ear. “There is a critical need to develop, validate and incorporate objective testing tools for the diagnosis of this neurological condition, and our results show that DystoniaNet may fill this gap.”
Diagnosis made easier
About 35 of every 100,000 people have isolated or primary dystonia, a prevalence likely underestimated due to the current challenges in diagnosing it. In some cases, dystonia can be a result of a neurological disorder, such as Parkinson’s disease or a stroke. However, the majority of isolated dystonia cases have no known cause and affect a single muscle group in the body. These so-called focal dystonias can lead to disability and problems with physical and emotional quality of life.
The study included three of the most common types of focal dystonia: laryngeal dystonia, characterized by involuntary movements of the vocal cords that can cause difficulties with speech (also called spasmodic dysphonia); cervical dystonia, which causes the neck muscles to spasm and the neck to tilt in an unusual manner; and blepharospasm, a focal dystonia of the eyelid that causes involuntary twitching and forceful eyelid closure.
Traditionally, a dystonia diagnosis is based on clinical observations, said Simonyan, who is also an associate neuroscientist at Massachusetts General Hospital. Previous studies have found that the agreement between clinicians on dystonia diagnoses based on clinical assessments is as low as 34 percent and have reported that about 50 percent of cases go misdiagnosed or underdiagnosed at an initial patient visit.
DystoniaNet uses deep learning, a particular type of artificial intelligence algorithm, to analyze data from an individual MRI and identify subtler differences in brain structure. The platform is able to detect clusters of abnormal structures in several regions of the brain known to control processing and motor commands. These small changes cannot be seen by the naked eye in an MRI, and the patterns are evident only through the platform’s ability to take 3D brain images and zoom in to their microstructural details.
“Our study suggests that the implementation of the DystoniaNet platform for dystonia diagnosis would be transformative for the clinical management of this disorder,” said study first author Davide Valeriani, HMS research fellow in otolaryngology head and neck surgery in the Dystonia and Speech Motor Control Laboratory at Mass Eye and Ear. “Importantly, our platform was designed to be efficient and interpretable for clinicians by providing the patient’s diagnosis, the confidence of the AI in that diagnosis and information about which brain structures are abnormal.”
DystoniaNet is a patent-pending proprietary platform developed by Simonyan and Valeriani, in conjunction with Mass General Brigham Innovation. The technology interprets an MRI scan for microstructural biomarkers in 0.36 seconds. DystoniaNet has been trained using the Amazon Web Services computational cloud platform. The researchers believe this technology can be easily translated into the clinical setting, such as by being integrated into an electronic medical record or directly into the MRI scanner software. If DystoniaNet finds a high probability of dystonia in an MRI, a physician can use this information to help confirm the diagnosis, pursue future actions and suggest a course of treatment without a delay. Dystonia cannot be cured, but some treatments can help reduce the incidence of dystonia-related spasms.
Future studies will look at more types of dystonia and will include trials at multiple hospitals to further validate the DystoniaNet platform in a larger number of patients.
This research was funded and supported by the National Institutes of Health (grants R01DC011805, R01DC012545 and R01NS088160), an Amazon Web Services Machine Learning Research Award and a gift from Keith and Bobbi Richardson.
Adapted from a Mass Eye and Ear news release.