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LactaLearn

2024
Team Members:
  • Veronica Kidwell
  • Eric Song
  • Shalika Subramanian
  • Rida Danish
  • Christina Heal-Kowal
  • Iris Zheng
  • Mackenzie Petersen
  • David Lu
Advisors:
  • Michelle Zwernemann
Sponsors:
  • Azadeh Farzin, MD
  • Monique Solieau-Burke, MD
  • Elizabeth Logsdon, PhD
  • Unnathi Annapurna Shashikumar

Abstract:

80% of new mothers want to exclusively breastfeed their infant; however, only 41% meet this goal. The most common reason why new mothers cease exclusive breastfeeding is because of concerns over adequate milk intake. At-home timing of feedings is often used to quantify this, but this method can be mentally exhausting and prone to error3. A more accurate assessment can be made using baby scales, but small changes in weight during feedings make it difficult to use a scale accurately without training. There is a need for an accessible solution that allows easy assessment of a newborn’s milk intake at home. We developed a mobile application that uses a deep learning model to estimate milk transfer efficiency. Through the detection of audible rhythmic swallowing, a sign of consistent milk intake, this app can provide parents with reassurance that their newborn is receiving enough milk when breastfeeding.

Project Video

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