Newswise — Despite recent advancements in the treatment of Parkinson’s Disease, it remains a challenge to accurately measure the progression of symptoms in this neurological disorder. While noticeable symptoms like tremors, stiffness and slowing of movement can be observed, there have previously been few precise ways to quantify changes in symptoms that can be used outside of research laboratories and in routine clinical practice. To provide more personalized treatment based on individuals’ disease state and progression, researchers at the University of California San Francisco (UCSF) developed a video-based analysis system enabled by machine learning (ML), to quantify and validate motor symptom severity in patients with Parkinson’s Disease (PD).
Their AI pipeline, running on standard clinical videos, was able to determine the severity of PD symptoms from very short video clips of just a few seconds. Their study appeared online in the June 25,2024 issue of NPJ Parkinson’s Disease . The system uses single-view, seconds-long videos recorded on devices such as smartphones, tablets, and digital cameras, eliminating the need for expensive, specialized equipment.
The researchers designed the framework to provide a comprehensive movement dataset and an interpretable video-based system able to predict high versus low PD motor symptom severity. The system automatically extracts a large array of features representing movement characteristics in raw, unedited video recordings .