The overall goal of this R01 project is to develop an automated assessment system that can capitalize on state of the art sensing technologies and machine learning algorithms to enable accurate and early detection of infants at risk for neurodevelopmental disabilities. In the USA, 1 in 10 infants are born at risk for these disabilities. For children with neurodevelopmental disabilities, early treatment in the first year of life improves long-term outcomes. However, we are currently held back by inadequacies of available clinical tests to measure and predict impairment. Existing tests are hard to administer, require specialized training, and have limited long- term predictive value. There is a critical need to develop an objective, accurate, easy-to-use tool for the early prediction of long-term physical disability. The field of pediatrics and infant development would greatly benefit from a quantitative score that would correlate with existing clinical measures used today to detect movement impairments in very young infants. To realize a new generation of tests that will be easy to administer, we will obtain large datasets of infants playing in an instrumented gym or simply being recorded while moving in a supine posture. Video and sensor data analyses will convert movement into feature vectors based on our knowledge of the problem domain. Our approach will use machine learning to relate these feature vectors to currently recommended clinical tests or other ground truth information. The power of this design is that algorithms can utilize many aspects of movement to produce the relevant scores. Our preliminary data allows us to lay the following aims: 1)Aim 1: To assess concurrent validity of a multimodal instrumented gym with existing clinical tools. Here, using 150 infants (75 with early brain injury and 75 controls), we will focus on converting data from an instrumented gym into estimates of the standard clinical tests; 2)Aim 2: To develop a computer vision-based algorithm to quantify infant motor performance from single camera video. Here using video data from 1200 infants (400 with early brain injury, 400 preterm without early brain injury, 400 controls), plus those gathered from Aim 1 and Aim 3, we will extract pose data from single-camera video recordings and convert these into kinematic features and relevant scores needed to classify infant movement; 3)Aim3: To discover the features related to long-term motor development. Here we will convert data collected longitudinally from 50 infants (25 with early brain injury and 25 controls) using both instrumented gym and video recordings into estimates standard clinical tests change over time and track features over developmental timescales. These three aims spearhead the use of real world behavior for movement scoring.
Our aims will bring us closer to a universal non-invasive test for early detection of neurodevelopmental disabilities and lay the groundwork for long-term prediction of disability. But above all, it promises to scale to infants worldwide, producing an affordable tool to aid in infant health assessment.
Increased survival rates of medically fragile infants lead to an increased number of children with functional impairments later in life. Early detection of motor delays or impairments provides the opportunity for early treatment which improves health outcomes. This study will use state of the art sensors combined with machine learning approaches to develop objective, accurate, easy-to-use tools for the early scoring of deficits and lays the foundation for the early prediction of physical disability.
