OBJECTIVES/GOALS: Can we detect Parkinson’s-disease-related motor impairments using computer vision and machine learning? METHODS/STUDY POPULATION: A sample of 29 people with Parkinson’s disease (PD) and 29 non-Parkinson’s disease (non-PD) controls were recruited from the University of Iowa Movement Disorders Clinic. Videos of 3 motor assessment tasks performed using the hands were recorded and hand location information was abstracted using the computer vision program MediaPipe. Measures from the raw data series and FFT were used as features to train a model using boosted trees to classify each video as PD or non-PD. Model performance was evaluated using leave-one-out cross-validation. Additionally, we used recursive feature elimination to reduce model complexity. RESULTS/ANTICIPATED RESULTS: A model using two features identified by recursive feature elimination yielded a model with an overall accuracy of 81% in cross-validation. In our sample, the model had 86.2% sensitivity, 75.9% specificity, and an AUC of 0.839. Additional improvement may be possible with more data processing, especially in the time-domain. DISCUSSION/SIGNIFICANCE: We built a classifier that was able to reliably and accurately discriminate between videos of motor assessments in people with Parkinson’s and people without. This may provide a low cost screening tool in rural areas or primary care clinics with limited access to neurologist expertise.