Artificial intelligence applied to the study of human milk and breastfeeding: a scoping review
Abstract Background 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 synth...
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| Format: | Article |
| Language: | English |
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BMC
2024-12-01
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| Series: | International Breastfeeding Journal |
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| Online Access: | https://doi.org/10.1186/s13006-024-00686-1 |
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| author | Sergio Agudelo-Pérez Daniel Botero-Rosas Laura Rodríguez-Alvarado Julián Espitia-Angel Lina Raigoso-Díaz |
| author_facet | Sergio Agudelo-Pérez Daniel Botero-Rosas Laura Rodríguez-Alvarado Julián Espitia-Angel Lina Raigoso-Díaz |
| author_sort | Sergio Agudelo-Pérez |
| collection | DOAJ |
| description | Abstract Background 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. Methods 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. Results 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. Conclusion 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. |
| format | Article |
| id | doaj-art-31eebbc297fd4ab2ad97263b981560e0 |
| institution | OA Journals |
| issn | 1746-4358 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | International Breastfeeding Journal |
| spelling | doaj-art-31eebbc297fd4ab2ad97263b981560e02025-08-20T02:31:00ZengBMCInternational Breastfeeding Journal1746-43582024-12-0119111510.1186/s13006-024-00686-1Artificial intelligence applied to the study of human milk and breastfeeding: a scoping reviewSergio Agudelo-Pérez0Daniel Botero-Rosas1Laura Rodríguez-Alvarado2Julián Espitia-Angel3Lina Raigoso-Díaz4Department of Pediatrics, School of Medicine, Universidad de La SabanaDepartment of Pediatrics, School of Medicine, Universidad de La SabanaDepartment of Pediatrics, School of Medicine, Universidad de La SabanaDepartment of Pediatrics, School of Medicine, Universidad de La SabanaDepartment of Pediatrics, School of Medicine, Universidad de La SabanaAbstract Background 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. Methods 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. Results 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. Conclusion 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.https://doi.org/10.1186/s13006-024-00686-1Artificial IntelligenceBreast FeedingHuman MilkMachine LearningNeural Network |
| spellingShingle | Sergio Agudelo-Pérez Daniel Botero-Rosas Laura Rodríguez-Alvarado Julián Espitia-Angel Lina Raigoso-Díaz Artificial intelligence applied to the study of human milk and breastfeeding: a scoping review International Breastfeeding Journal Artificial Intelligence Breast Feeding Human Milk Machine Learning Neural Network |
| title | Artificial intelligence applied to the study of human milk and breastfeeding: a scoping review |
| title_full | Artificial intelligence applied to the study of human milk and breastfeeding: a scoping review |
| title_fullStr | Artificial intelligence applied to the study of human milk and breastfeeding: a scoping review |
| title_full_unstemmed | Artificial intelligence applied to the study of human milk and breastfeeding: a scoping review |
| title_short | Artificial intelligence applied to the study of human milk and breastfeeding: a scoping review |
| title_sort | artificial intelligence applied to the study of human milk and breastfeeding a scoping review |
| topic | Artificial Intelligence Breast Feeding Human Milk Machine Learning Neural Network |
| url | https://doi.org/10.1186/s13006-024-00686-1 |
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