We've updated our Privacy Policy to make it clearer how we use your personal data. We use cookies to provide you with a better experience. You can read our Cookie Policy here.


Pacifier and AI Assess Newborns’ Breastfeeding Mechanics

A woman breastfeeding.
Credit: iStock.
Listen with
Register for free to listen to this article
Thank you. Listen to this article using the player above.

Want to listen to this article for FREE?

Complete the form below to unlock access to ALL audio articles.

Read time: 2 minutes

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.

Want more breaking news?

Subscribe to Technology Networks’ daily newsletter, delivering breaking science news straight to your inbox every day.

Subscribe for FREE

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. 

Is tongue-tie surgery needed? 

An estimated 7% of babies are diagnosed with a condition called tongue-tie, in which the connection between the tongue and the floor of the mouth is too strong and limits tongue movement. The condition presents challenges with breastfeeding and often requires surgery, known as a frenotomy, where the connective tissue between the tongue and the floor of the mouth are cut.

Data from the device showed there was not a change pre and post-surgery for half of the infants examined who had undergone a frenotomy. The other half, whose data patterns were abnormal, and whom the algorithms identified as needing a frenotomy, did benefit from the operation with much improved suckling behavior after the surgery. 

These results suggest that, in some cases, surgical interventions could potentially be prevented.

Data from the device also flagged abnormal nursing behavior in five babies, which had not been found during a clinical exam.

These findings are important because frenotomies have had a tenfold increase in less than a decade. “Our data show that frenotomies are not a blanket solution for breastfeeding difficulties,” Walsh added. 

How the study was conducted 

The proof of concept study was approved by UC San Diego’s Internal Review Board. Parents of healthy full-term infants under 30 days old were recruited from the UC San Diego Center for Voice and Swallowing, UC San Diego Health La Jolla Pediatrics, and the UC San Diego Jacobs Medical Center.

In all, the 91 participants in the proof of concept study were recruited during routine postpartum care with their general pediatrician at UC San Diego Health or while consulting with feeding specialists at their respective locations. Infant inclusion criteria included full-term healthy infants establishing breastfeeding and without significant birth or postpartum complications.

Clinicians were blinded to device data in this study and performed evaluations solely based on standard practice. After clinical assessments, parents were provided the opportunity to introduce the modified pacifier for a 60 second measurement of their infant’s intraoral suckling vacuum.

Next steps

Next steps include conducting a clinical trial outside of UC San Diego Health with the ultimate goal of making both the device and algorithm widely available in pediatric practices, where they could be used during an infant's first visit.

Friend and Walsh are in the process of starting a company to license the technology from UC San Diego and bring it to the clinic. 

Reference: P. Truong, E. Walsh, V. P. Scott, M. Leff, A. Chen, J. Friend. Application of Statistical Analysis and Machine Learning to Identify Infants’ Abnormal Suckling Behavior. IEEE J Translat Eng Health Med. 2024:1-1. doi: 10.1109/JTEHM.2024.3390589

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source. Our press release publishing policy can be accessed here.