Abstract Details

Title Automation of Movement Disorders Society Unified Parkinson’s Disease Rating Scale Motor Subscale (MDS-UPDRS part III) Scoring via Machine Learning

Topic Movement Disorders

Presentation(s) P9 - Poster Session 9 (11:45 AM-12:45 PM)

Poster/Presentation
Number 5-030

Objective To describe a prototype AI algorithm trained to quantify movement from natural single-camera smartphone video into MDS-UPDRS motor scores.

Background The MDS-UPDRS motor subscale is a widely used tool in clinical and research assessments of Parkinsonian disorders. However, it requires specially trained raters and captures only predefined movements. A machine learning model could capture both standardized MDS-UPDRS part III assessments and currently unquantified metrics such as variability in joint movement and finger tap decrement rate.

Design/Methods Videos of individuals with Parkinson's disease (PD) performing MDS-UPDRS were ingested by the algorithm. Object detection models (YOLO real-time detection) classified gestures. Hand and body landmarks were detected by MediaPipe. These spatial features were used to calculate motor metrics for training the AI algorithm (AWS Sagemaker) using a Random Forest Classifier. An 80/20 training-validation split was used, and performance was compared with a 60/40 split. Items not filmed—rigidity and postural stability—were excluded. Initial analysis focused on hand movements and tremor.

Results From 71 videos (30 unique patients), 382 video segments were extracted. Hand movements were analyzed, and with an 80/20 training split, concordance with expert rater scores was 0.70 for finger tapping, 0.85 for hand opening/closing, and 0.75 for pronation/supination. In the 60/40 split, these scores were 0.50, 0.83, and 0.75, respectively. The AI’s performance met the endpoint of matching expert MDS-UPDRS scores within a ±1.0-point error margin.

Conclusions This prototype demonstrates the feasibility of automating MDS-UPDRS hand movement scoring using single-camera smartphone video. Sample size was the primary limitation, and additional data will enable modeling of more MDS-UPDRS items. This method could also reduce scoring subjectivity, e.g. rater tendency to preferentially score in the middle range (1-2), thereby increasing precision and objectivity. This approach could improve access to accurate movement assessments across clinical and research settings, enhancing both variability and resolution.

Authors/Disclosures
Tara Najafi PRESENTER	Ms. Najafi has nothing to disclose.
Taylor Peabody, MD	Dr. Peabody has nothing to disclose.
Derrick James	Mr. James has nothing to disclose.
Allan Brener, N/A	No disclosure on file
Kyle Bogdan	No disclosure on file
Natalia Peshekhodko (Slalom Build)	No disclosure on file
Corneliu C. Luca, MD (University of Miami)	Dr. Luca has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Boston Scientific. Dr. Luca has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Signant Health. Dr. Luca has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Abbott.
Ihtsham Haq, MD, FAAN (University of Miami Miller School of Medicine)	The institution of Dr. Haq has received research support from NINDS. The institution of Dr. Haq has received research support from the Parkinson's Foundation.

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