Evaluating the feasibility of an innovative point-of-care screening tool for detection of infant motor delay within the newborn period

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The onset of many chronic neurodevelopmental conditions, such as cerebral palsy, neuromuscular conditions, and other neurogenetic disorders occur within the perinatal phase of life. Detection of these conditions, during infancy, when brain plasticity is at its highest, has the potential to dramatically improve long term outcomes. In the United States (U.S.) the age of diagnosis of conditions such as cerebral palsy is around 2 years of age for children who had known perinatal risk factors. As many as half of the children with a disability, however, are from an uncomplicated pregnancy and the diagnosis typically occurs even later in life. Alarmingly, a diagnosis for a child from an underserved community can be as late as 5 years of age. The traditional paradigm for diagnosis of these conditions, uses a ‘wait-and-see’ approach until the child eventually misses motor milestones, thus the infant has missed critical months of possible early intervention. For infants with perinatal risk factors, a diagnosis is sometime achieved through the use of costly technologies, such as magnetic resonance imaging; or specialized evaluation techniques, such as the General Movements Assessment (GMA). However, these tests are typically only available in tertiary care centers. Thus, a point of care newborn screening system could transform the diagnostic process, allowing every child the opportunity for early detection of aberrant movement patterns within the newborn period. Our innovative system, BabySure uses an artificial intelligence model to evaluate infant movement characteristics from a simple, non-invasive video recording. Our pilot data has shown that BabySure can evaluate and categorize spontaneous movements as healthy or aberrant in infants before the age of 6 months. To ensure every baby has the opportunity to receive an early, accurate diagnosis, we will pilot a newborn screening program in a community newborn nursery. BabySure uses automated skeletal tracking to extract positional coordinates of 33 skeletal landmarks from each video frame of the infant’s spontaneous movements (including points on the extremities, head, face, and trunk). An essential part of our project is to continue our efforts to validate our system on a variety of skin tones as historically, computer vision has not been sufficiently accurate on darker skin. To train our model to identify aberrant movement we will use the gold-standard GMA to classify infants in aberrant or healthy movement training groups. Our analysis plan will prototype three artificial intelligence base models to ensemble a single classifier (each with two heads: regression and classification) including fine tuning convolutional neural networks pre-trained for image processing and custom-training models. The final model will output a motor function score that will classify the infant movement as typical or aberrant. In addition, we will evaluate the intra- and inter-day variability of results to inform future recommendations for optimal timing or sequence of movement screening across the newborn period.