Hyperkinetic movement disorders (HMDs) in children, including dystonia (abnormal twisting) and chorea (irregular, random movements), pose significant diagnostic challenges due to overlapping clinical features. The prevalence of dystonia ranges from 2 to 50 per million, and chorea from 5 to 10 per 100,000. These conditions are often diagnosed with delays averaging 4.75 to 7.83 years. Traditional diagnostic methods depend on clinical history and expert physical examinations, but specialized tests are ineffective due to the complex pathophysiology of these disorders. This study develops a neural network model to differentiate between dystonia and chorea from video recordings of paediatric patients performing motor tasks. The model integrates a Graph Convolutional Network (GCN) to capture spatial relationships and Long Short-Term Memory (LSTM) networks to account for temporal dynamics. Attention mechanisms were incorporated to improve model interpretability. The model was trained and validated on a dataset of 50 videos (31 chorea-predominant, 19 dystonia-predominant) collected under regulatory approval from Guy's and St Thomas' NHS Foundation Trust. The model achieved 85% accuracy, 81% sensitivity, and 88% specificity at 15 frames per second. Attention maps highlighted the model's ability to correctly identify involuntary movement patterns, with misclassifications often due to occluded body parts or subtle movement variations. This work demonstrates the potential of deep learning to improve the accuracy and efficiency of HMD diagnosis and could contribute to more reliable, interpretable clinical tools.

In a new study published November 28 in Annals of Neurology, an artificial intelligence platform called DystoniaBoTXNet used brain MRIs to automatically identify which patients would respond to botulinum toxin treatment with 96.3 percent accuracy. Such a platform can inform clinicians' treatment decisions, according to senior study author Kristina Simonyan, MD, PhD, Dr med, director of Laryngology Research at Mass Eye and Ear, a member of Mass General Brigham, and professor of Otolaryngology-Head and Neck Surgery at Harvard Medical School. "Typically, a patient with dystonia would undergo a series of dose- and location-finding injections to determine whether botulinum toxin relieves their symptoms. Injections are painful and costly," said Dr. Simonyan. "Yet, some may find no benefits from this treatment despite multiple injection attempts, while some might benefit from injections but give up after only one dose or forgo the treatment altogether. With this artificial intelligence algorithm, we can empower clinicians and patients in their therapeutic decision-making by providing them with an objective tool to replace the trial-and-error approach to botulinum toxin efficacy."

This paper reflects on the effect of several categories of medical conditions on human voice, focusing on those that may be hypothesized to have effects on voice, but for which the changes themselves may be subtle enough to have eluded observation in standard analytical examinations of the voice signal. It presents three categories of techniques that can potentially uncover such elusive biomarkers and allow them to be measured and used for predictive and diagnostic purposes. These approaches include proxy techniques, model-based analytical techniques and data-driven AI techniques.

For most people living through the latest pandemic, the urgent questions are often "when questions." When indoor establishments should lift capacity limits. When mask requirements should be dropped. When family, friends, and strangers should reconnect across household lines. For millions of other people, the question is more like whether. Whether there ever will be an opening. Whether they will be welcome participants. Whether the reengineered social relationships for post-pandemic life will include them. Because when the physical world is utterly open, the social world can be closed to these people.

Mass Eye and Ear researchers have discovered a unique diagnostic tool that can detect dystonia from MRI scans. It is the first technology of its kind to provide an objective diagnosis of the disorder. Dystonia is a potentially disabling neurological condition which causes involuntary muscle contractions, driving to abnormal movements and postures. It is often mistreated and sometimes takes people up to 10 years to get a correct diagnosis. A new study by PNAS researches shows that they have developed an AI-based deep learning platform on September 28, called DystoniaNet to compare brain MRIs of 612 people.