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Integrating AI in Lactation and Breastfeeding Support

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Speakers: Jessica de Souza, Kristina Chamberlain, Bárbara Tideman Sartorio Camargo and Kelly Pereira Coca

Presentation Handouts

Presentation_handouts_Sunday.pdf

Abstract

Artificial intelligence (AI) is expanding access to lactation care, especially following the COVID-19 pandemic, which increased the need for remote consultations. Many breastfeeding issues have visible physical signs assessed clinically to guide care decisions, including mastitis and nipple injuries. Deep learning (DL) algorithms have shown potential in classifying these conditions through images, offering support to lactation professionals in practice and training.This work aims to (a) review studies using AI in lactation care, (b) present two DL models that identify general breastfeeding issues and nipple injuries, and (c) discuss challenges in data access, accuracy, and user trust in clinical AI systems, along with opportunities for improved outcomes in digital lactation care.

Learning Objectives:

Identify current applications of artificial intelligence in lactation care and its potential impact in image-based classification, patient triage, and lactation training.
Assess the potential benefits of using algorithms in classifying breastfeeding complications, such as abscesses, mastitis, dermatological issues, and nipple damage, while recognizing the current limitations of these tools.
Demonstrate how AI tools can support clinical decision-making and education in lactation care when their limitations are addressed through multidisciplinary collaboration.

Additional Resources

Apps you might not have heard of

LactApp (for patients and providers) - https://lactapp.es/

Huckleberry App - https://huckleberrycare.com/

Write better and more specific AI prompts for your research, practice, and in general - https://promptperfect.jina.ai/interactive

Chat with a tool that provides references that exist and accurate information - https://scispace.com/

Tools for nipple lesion classification

Classification Instrument for Nipple-Areolar Lesions (ILMA) :

Cervellini MP, Coca KP, Gamba MA, Marcacine KO, Abrao ACFV. Construction and validation of an instrument for classifying nipple and areola complex lesions resulting from breastfeeding. Rev Bras Enferm. 2021;75(1):e20210051.

Seven Signs of Nipple Trauma & Small Changes:

Nakamura, M., Asaka, Y., Ogawara, T., & Yorozu, Y. (2018). Nipple skin trauma in breastfeeding women during postpartum week one. Breastfeeding medicine, 13(7), 479-484.

Nipple Trauma Score (NTS):

Abou-Dakn, M., Fluhr, J. W., Gensch, M., & Woeckel, A. (2010). Positive effect of HPA lanolin versus expressed breastmilk on painful and damaged nipples during lactation. Skin pharmacology and physiology, 24(1), 27-35.

Visual Analog Scale of Pain:

Scott, J., & Huskisson, E. C. (1976). Graphic representation of pain. pain, 2(2), 175-184.

LATCH:

Jensen, D., Wallace, S., & Kelsay, P. (1994). LATCH: a breastfeeding charting system and documentation tool. Journal of Obstetric, Gynecologic, & Neonatal Nursing, 23(1), 27-32.

References

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