Sergio Agudelo-Pérez, Daniel Botero-Rosas, Laura Rodríguez-Alvarado, Julián Espitia-Angel, Lina Raigoso-Díaz
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.
Pediatr Crit Care Med. 2018 Oct;19(10):e495-e503
[PMID:
30052552]
Curr Diab Rep. 2021 Dec 13;21(12):61
[PMID:
34902070]
Healthcare (Basel). 2022 Apr 14;10(4):
[PMID:
35455909]
J Pediatr. 2018 Dec;203:190-196.e21
[PMID:
30293638]
J Pediatr. 2023 Oct;261:113562
[PMID:
37329981]
Cochrane Database Syst Rev. 2016 Nov 25;11:CD003519
[PMID:
27885658]
Ann Intern Med. 2018 Oct 2;169(7):467-473
[PMID:
30178033]
Lancet. 2016 Jan 30;387(10017):475-90
[PMID:
26869575]
Int J Gen Med. 2024 May 01;17:1765-1771
[PMID:
38706749]
BMC Pregnancy Childbirth. 2022 Jul 14;22(1):562
[PMID:
35836199]
Int Breastfeed J. 2021 Jan 4;16(1):2
[PMID:
33397423]
Front Public Health. 2022 Sep 30;10:880034
[PMID:
36249249]
Lancet Glob Health. 2016 Apr;4(4):e266-75
[PMID:
27013313]
J Pers Med. 2023 Jun 05;13(6):
[PMID:
37373940]
J Nutr. 2021 Jul 1;151(7):2075-2083
[PMID:
33847342]
Int Breastfeed J. 2022 Jul 7;17(1):49
[PMID:
35799253]
BMJ Health Care Inform. 2023 Dec 21;30(1):
[PMID:
38135293]
Can J Public Health. 2014 May 09;105(3):e179-85
[PMID:
25165836]
Anal Bioanal Chem. 2010 Jul;397(6):2367-74
[PMID:
20490467]
Talanta. 2013 Nov 15;116:315-21
[PMID:
24148409]
BMJ Open. 2018 Jun 22;8(6):e020384
[PMID:
29934384]
JMIR Mhealth Uhealth. 2018 Jan 09;6(1):e7
[PMID:
29317380]
Nature. 2017 Feb 2;542(7639):115-118
[PMID:
28117445]
Front Endocrinol (Lausanne). 2022 Jun 09;13:890307
[PMID:
35757428]
Lancet. 2016 Jan 30;387(10017):416
[PMID:
26869553]
Circ Res. 2017 Aug 4;121(4):317-319
[PMID:
28775006]
Pharm Res. 2006 Jan;23(1):41-8
[PMID:
16308669]
Matern Child Nutr. 2022 Jul;18(3):e13364
[PMID:
35586991]
Healthcare (Basel). 2024 Mar 18;12(6):
[PMID:
38540644]
J Zhejiang Univ Sci B. 2017 May;18(5):393-401
[PMID:
28471111]
Nutrients. 2017 Aug 07;9(8):
[PMID:
28783113]
Foods. 2022 Dec 19;11(24):
[PMID:
36553847]
J Am Med Inform Assoc. 2022 Apr 13;29(5):1000-1010
[PMID:
35137107]
Nature. 2017 Feb 15;542(7641):348-351
[PMID:
28202961]
Pharm Res. 2023 Mar;40(3):711-719
[PMID:
36720832]
J Pharm Biomed Anal. 2002 Jun 20;29(1-2):103-19
[PMID:
12062670]
PLoS Med. 2019 Aug 30;16(8):e1002904
[PMID:
31469827]
Lancet Child Adolesc Health. 2021 Sep;5(9):619-630
[PMID:
34245677]
Ecotoxicol Environ Saf. 2019 May 15;172:341-347
[PMID:
30721878]
Chin Med J (Engl). 2020 Feb 5;133(3):358-360
[PMID:
31929357]
Indian J Dermatol. 2020 Sep-Oct;65(5):365-370
[PMID:
33165420]
J Family Med Prim Care. 2019 Jul;8(7):2328-2331
[PMID:
31463251]
Breastfeed Med. 2015 Mar;10(2):118-23
[PMID:
25565242]
Acad Emerg Med. 2018 Dec;25(12):1463-1470
[PMID:
30382605]
Int J Environ Res Public Health. 2022 Dec 14;19(24):
[PMID:
36554670]
Comput Methods Programs Biomed. 2022 Jun;221:106837
[PMID:
35544962]
Chemosphere. 2004 Mar;54(10):1375-82
[PMID:
14659939]
