Artificial intelligence for personalized multiple micronutrient supplementation in maternal health

Area of Science:

• Reproductive Health and Nutrition
• Artificial Intelligence in Healthcare
• Maternal-Fetal Medicine

Background:

• Maternal undernutrition and micronutrient deficiencies pose significant global health risks, impacting pregnancy outcomes and long-term health for mothers and offspring.
• Standard multiple micronutrient supplementation (MMS) offers benefits over iron-folic acid but often fails to account for individual variations in nutritional status and health profiles.
• Existing MMS strategies lack personalization, potentially limiting their effectiveness in diverse populations.

Purpose of the Study:

• To propose a conceptual model for leveraging artificial intelligence (AI) to enhance personalized multiple micronutrient supplementation (MMS) strategies during preconception and pregnancy.
• To explore how AI can integrate multimodal health data for risk stratification and inform more responsive maternal nutrition programs.
• To introduce the concept of a 'nutritional digital twin' for simulating micronutrient needs and predicting outcomes under various supplementation scenarios.

Main Methods:

• Aggregation and analysis of multimodal health data, including electronic health records (EHRs), wearable sensor data, app logs, genomic markers, and sociodemographic information, using AI systems.
• Application of deep learning models to identify complex patterns (e.g., diet-genome interactions) and natural language processing (NLP) to extract insights from unstructured clinical data.
• Utilizing digital maternal health tools (mobile apps, wearables) for real-time data collection and improved adherence to supplementation regimens.

Main Results:

• AI can stratify women by risk of micronutrient insufficiencies, identify groups benefiting from additional support, and inform personalized MMS plans.
• The 'nutritional digital twin' concept allows for simulation of micronutrient needs and prediction of maternal-fetal outcomes, enabling scenario-based clinical decision-making.
• AI-driven approaches can reveal 'hidden hunger' patterns and predictors of low supplement uptake, informing targeted public health interventions, particularly in low-resource settings.

Conclusions:

• AI-enhanced personalized MMS holds transformative potential for maternal nutrition care in both high- and low-resource settings, moving towards precision maternal nutrition.
• Ethical considerations, credibility through validation, and fairness through diverse data representation are critical for the successful implementation of AI in maternal nutrition.
• Integrating AI and digital health innovations into antenatal care can improve pregnancy nourishment, reduce mortality, and promote healthier futures for mothers and children.