Automated Prediction of Infant Cognitive Development Risk by Video: A Pilot Study

Objective: Cognition is an essential human function, and its development in infancy is crucial. Traditionally, pediatricians used clinical observation or medical imaging to assess infants' current cognitive development (CD) status. The object of pediatricians' greater concern is however their future outcomes, because high-risk infants can be identified early in life for intervention. However, this opportunity has not yet been realized. Fortunately, some recent studies have shown that the general movement (GM) performance of infants around 3-4 months after birth might reflect their future CD status, which gives us an opportunity to achieve this goal by cameras and artificial intelligence. Methods: First, infants' GM videos were recorded by cameras, from which a series of features reflecting their bilateral movement symmetry (BMS) were extracted. Then, after at least eight months of natural growth, the infants' CD status was evaluated by the Bayley Infant Development Scale, and they were divided into high-risk and low-risk groups. Finally, the BMS features extracted from the early recorded GM videos were fed into the classifiers, using late infant CD risk assessment as the prediction target. Results: The area under the curve, recall and precision values reached 0.830, 0.832, and 0.823 for two-group classification, respectively. Conclusion: This pilot study demonstrates that it is possible to automatically predict the CD of infants around the age of one year based on their GMs recorded early in life. Significance: This study not only helps clinicians better understand infant CD mechanisms, but also provides an economical, portable and non-invasive way to screen infants at high-risk early to facilitate their recovery.