AI-Driven Human Milk Fortification for Preterm Infants

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Solution Overview

Problem

Current methods for determining optimal and personalized nutrition for preterm infants are inadequate, as they fail to account for individual clinical data and nutritional needs, leading to potential growth faltering and increased disease risk due to insufficient or inappropriate macronutrient and micronutrient intake in human milk fortification.

Innovation Solution

A system utilizing sensors to analyze human milk macronutrient and micronutrient composition, combined with an AI server that compares the data to nutritional guidelines and historical clinical data to provide personalized fortification recommendations, including specific fortifiers and feeding protocols, while also monitoring growth and disease risk scores.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If standard nutritional guidelines are used for fortification, then feeding protocol is simplified, but individual nutritional needs and disease risk factors are not addressed

Engineering Contradiction:

Improvepersonalization of nutritionVSAvoidcomplexity of fortification system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary analysis of human milk composition and infant clinical data before fortification decisions are made. Sensors analyze macronutrient and micronutrient content in advance, and the AI server pre-calculates disease risk scores based on clinical data, enabling personalized fortification protocols to be established before feeding begins.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements continuous feedback loops where growth measurements and clinical data are continuously monitored, analyzed by the AI server, and used to adjust fortification recommendations. This closed-loop feedback enables the system to adapt to individual infant needs while maintaining manageable complexity through automated decision-making.

Inventive Principle:

Principle #23Feedback

2Quantity of substance

If human milk is used without fortification, then natural nutrition is preserved, but nutrient levels are insufficient for preterm infant growth needs

Engineering Contradiction:

Improvenutrient concentrationVSAvoidfortification process complexity

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The system dynamically adjusts fortification parameters based on sensor analysis of human milk composition and AI-generated recommendations. Macronutrient and micronutrient fortification levels are customized for each infant based on their specific needs, growth rate, and disease risk factors, transforming the fixed fortification process into an adaptive system.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The AI server acts as an intermediary between human milk analysis and fortification decisions. It processes sensor data, compares it against nutritional guidelines and clinical data, and generates personalized fortification recommendations, simplifying the complex decision-making process for healthcare providers.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If monitoring and checking protocols are implemented, then nutritional adequacy is verified, but time delays may occur that affect infant survival

Engineering Contradiction:

Improvenutritional protocol accuracyVSAvoidtime to implement feeding protocol

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual monitoring and checking processes with automated sensor-based analysis and AI-driven decision support. This substitution eliminates time-consuming manual measurements and calculations, providing rapid, accurate nutritional assessment and fortification recommendations that can be implemented immediately.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Measurement precision

If clinical data analysis is performed to identify disease risks, then personalized nutrition can be optimized, but data processing complexity increases

Engineering Contradiction:

Improvedisease risk assessment accuracyVSAvoiddata processing system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20230352146A1Systems and methods for optimal personalized infant nutrition, disease prevention, and growth monitoring

Publication Date: 2023.11.02 TELLSPEC LTD

AI summary

A system may include one or more sensors which analyze a sample of human milk to be fed to a specific infant. The system may further include an artificial intelligence server including one or more processors implementing several artificial intelligence processes. The artificial intelligence server may receive sensor data generated by one or more sensors and analyze the sensor data to identify constituent elements of macronutrients and micronutrients in the sample of milk. The artificial intelligence server may further compare the constituent elements of macronutrients and or micronutrients in the sample of milk with nutritional guidelines and with nutritional protocols obtain from historical clinical data from infants with a clinical profile similar to that of the specific infant. The artificial intelligence server may further identify one or more disease risk scores for the specific infant. Methods implemented by the system are also disclosed.