Agudelo-Pérez Sergio, Botero-Rosas Daniel, Rodríguez-Alvarado Laura, Espitia-Angel Julián, Raigoso-Díaz Lina
Department of Pediatrics, School of Medicine, Universidad de La Sabana, Chía, Cundinamarca, Colombia.
Int Breastfeed J. 2024 Dec 6;19(1):79. doi: 10.1186/s13006-024-00686-1.
Breastfeeding rates remain below the globally recommended levels, a situation associated with higher infant and neonatal mortality rates. The implementation of artificial intelligence (AI) could help improve and increase breastfeeding rates. This study aimed to identify and synthesize the current information on the use of AI in the analysis of human milk and breastfeeding.
A scoping review was conducted according to the PRISMA Extension for Scoping Reviews guidelines. The literature search, performed in December 2023, used predetermined keywords from the PubMed, Scopus, LILACS, and WoS databases. Observational and qualitative studies evaluating AI in the analysis of breastfeeding patterns and human milk composition have been conducted. A thematic analysis was employed to categorize and synthesize the data.
Nineteen studies were included. The primary AI approaches were machine learning, neural networks, and chatbot development. The thematic analysis revealed five major categories: 1. Prediction of exclusive breastfeeding patterns: AI models, such as decision trees and machine learning algorithms, identify factors influencing breastfeeding practices, including maternal experience, hospital policies, and social determinants, highlighting actionable predictors for intervention. 2. Analysis of macronutrients in human milk: AI predicted fat, protein, and nutrient content with high accuracy, improving the operational efficiency of milk banks and nutritional assessments. 3. Education and support for breastfeeding mothers: AI-driven chatbots address breastfeeding concerns, debunked myths, and connect mothers to milk donation programs, demonstrating high engagement and satisfaction rates. 4. Detection and transmission of drugs in breast milk: AI techniques, including neural networks and predictive models, identified drug transfer rates and assessed pharmacological risks during lactation. 5. Identification of environmental contaminants in milk: AI models predict exposure to contaminants, such as polychlorinated biphenyls, based on maternal and environmental factors, aiding in risk assessment.
AI-based models have shown the potential to increase breastfeeding rates by identifying high-risk populations and providing tailored support. Additionally, AI has enabled a more precise analysis of human milk composition, drug transfer, and contaminant detection, offering significant insights into lactation science and maternal-infant health. These findings suggest that AI can promote breastfeeding, improve milk safety, and enhance infant nutrition.
母乳喂养率仍低于全球推荐水平，这种情况与较高的婴儿和新生儿死亡率相关。人工智能（AI）的应用有助于提高母乳喂养率。本研究旨在识别和综合当前关于人工智能在母乳和母乳喂养分析中的应用信息。
根据PRISMA扩展的范围综述指南进行了一项范围综述。2023年12月进行了文献检索，使用了来自PubMed、Scopus、LILACS和WoS数据库的预定关键词。开展了评估人工智能在母乳喂养模式和母乳成分分析中的观察性和定性研究。采用主题分析对数据进行分类和综合。
纳入了19项研究。主要的人工智能方法是机器学习、神经网络和聊天机器人开发。主题分析揭示了五个主要类别：1. 纯母乳喂养模式的预测：决策树和机器学习算法等人工智能模型识别影响母乳喂养行为的因素，包括母亲的经验、医院政策和社会决定因素，突出了可采取行动的干预预测因素。2. 母乳中宏量营养素的分析：人工智能能高精度预测脂肪、蛋白质和营养成分含量，提高了母乳库和营养评估的运作效率。3. 对母乳喂养母亲的教育和支持：人工智能驱动的聊天机器人解决母乳喂养问题，破除谣言，并将母亲与母乳捐赠项目联系起来，显示出高参与度和满意度。4. 母乳中药物的检测和传递：包括神经网络和预测模型在内的人工智能技术识别药物转移率，并评估哺乳期的药理学风险。5. 母乳中环境污染物的识别：人工智能模型根据母亲和环境因素预测多氯联苯等污染物的暴露情况，有助于风险评估。
基于人工智能的模型已显示出通过识别高危人群并提供定制支持来提高母乳喂养率的潜力。此外，人工智能能够对母乳成分、药物传递和污染物检测进行更精确的分析，为泌乳科学和母婴健康提供了重要见解。这些发现表明，人工智能可以促进母乳喂养、提高母乳安全性并改善婴儿营养。
