Newswise — A modified pacifier and AI algorithms to analyze the data it produces could determine if newborns are learning the proper mechanics of nursing, a recent study shows. Specifically, the researchers from the University of California San Diego measured if babies are generating enough suckling strength to breastfeed and whether they are suckling in a regular pattern based on eight independent parameters. The results, published in the April 18 online edition of IEEE Journal of Translational Engineering in Health and Medicine , give researchers objective data that shows standard assessments can be improved and could potentially prevent surgical interventions.
Currently, to determine if an infant is feeding properly, clinicians rely on two measures. One is objective: is the baby gaining enough weight? The other is more subjective: clinicians put a finger in the baby’s mouth and evaluate how well the baby is sucking on that finger. “The method we developed with our clinical partners replaces this subjective assessment with objective data,” said James Friend, a professor in the Department of Mechanical and Aerospace Engineering and the Department of Surgery at UC San Diego and one of the paper’s senior authors.
The testing method has two components. One is a device made up of a simple pacifier, connected to a 36-inch-long tube connected in turn to a vacuum sensor and a chip that collects the data from the sensor. The device can connect to any laptop.
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