56 Automated assessment of selective motor control in preterm infants using computer vision

Objectives/Goals: Cerebral palsy (CP) results in impaired selective motor control (SMC), or the ability to move joints independently. This study leverages computer vision algorithms to create a scalable, low-cost, and accessible tool that automatically quantifies SMC behavior in infants born preterm, allowing for earlier screening and intervention for CP. Methods/Study Population: In single-camera videos of 600 15-week-old infants, all of whom have standardized clinical SMC scores available, human pose estimation algorithms will be used to derive 3D joint trajectories and kinematic metrics. These features will be used to train and cross-validate a machine learning model aimed at automating clinical scoring of SMC from single-camera video recordings of infants. Then, this model will be used to automatically score the SMC in longitudinal single-camera videos of 47 preterm infants (collected every two weeks from birth until 16 weeks of age). The SMC score trajectories between infants who eventually received a CP diagnosis and those who did not will be compared to establish an inflection point at which SMC development begins to diverge, establishing an optimal timepoint for intervention. Results/Anticipated Results: Preliminary results show that 3D angles of 22 joints across the infant’s body can be estimated in each frame of a 1 to 3 minute single-camera video. We anticipate that the error between the 3D angle estimated from a custom fitted kinematic model and the 2D angle calculated using human pose estimation measures will be less than 5 degrees for all joints measured. With a dataset of over 1,500 videos from over 600 infants, we expect that the trained machine learning model will achieve a sensitivity and specificity of over 90% in predicting the clinical SMC score. Finally, we expect that the automated, longitudinal SMC score trajectories will begin to significantly deviate in infants with a later CP diagnosis, from those without, at around term age (40 weeks gestational age), based on recent clinical findings. Discussion/Significance of Impact: We provide the first automated, quantitative analysis of SMC from video recordings that can be used within real-world settings such as the NICU, at home, or at daycare. This work will result in a cost-effective, precise, smartphone-accessible tool that empowers earlier, more equitable diagnosis and treatment planning.