Brain research and advocacy non-profit Cohen Veterans Bioscience (CVB) announces the publication of results from its digital health research program analyzing data from the Parkinson’s Progression Markers Initiative (PPMI) to detect the presence or absence of Parkinson’s disease (PD).

Published in the Journal Sensors on September 9, 2022, the article, titled “Deep Learning for Daily Monitoring of Parkinson’s Disease Outside the Clinic Using Wearable Sensors” presents the results of a novel machine-learning analysis led by CVB with support from The Michael J. Fox Foundation (MJFF), demonstrating the potential of using sensors, human activity recognition, and deep-learning to aid in the classification of PD.

PD is one of the most common and fastest growing neurological disorders that results in a progressive decline in both motor and non-motor (e.g., cognition and mood) symptoms. Since there are currently no objective biomarkers in PD, diagnosis is complicated and typically involves clinically administered subjective questionnaires to assess severity of symptoms, potentially leading to symptoms being undetected or misclassified.

Sensor technology has shown promise in aiding in detection and classification of diseases like PD but have very limited validation in real-world settings. As part of the Parkinson’s Progression Markers Initiative (PPMI) study cohort, investigators collected data passively and continuously using the Verily Study Watch in a subject’s natural environment. Using this data, researchers at CVB in collaboration with PPMI investigators, used novel deep learning artificial intelligence (AI) techniques to explore the potential for predicting the presence of PD through real-life activity.

Results of this research are promising. In a pilot sample, investigators were able to discriminate between subjects with and without a PD diagnosis with near 90% accuracy on single walk-like measures and 100% accuracy when assessing data accumulated over the duration of one day.

“This study shows the feasibility of leveraging unconstrained and unlabeled wearable sensor data to accurately detect Parkinson’s disease using powerful deep learning methods,” states Lee Lancashire, Principal Investigator of the study and Chief Information Officer at CVB. “Through this combination of wearables and AI, we are one step closer to monitoring individual healthcare-related activity, such as motor function outside the clinic, unleashing the potential for early detection and diagnosis of diseases such as Parkinson’s disease.”

The results from this novel proof of concept study can pave the way for the use of sensors as a valuable tool in monitoring PD symptom progression more objectively and more frequently. States Co-Author Mark Frasier, the Chief Scientific Officer at MJFF: “Although additional studies are needed, we are excited about the potential of using sensor data obtained through a patients’ normal activity to enable physicians to monitor and classify PD symptoms through easy to obtain objective measures which can be used to improve clinical decision making and guide therapeutic interventions.”