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  1. Home
  3. Machine Learning-driven Computer Vision System For Automated Fat And Energy Quantification In Human Milk Microcapillaries.
  1. Home
  3. Machine Learning-driven Computer Vision System For Automated Fat And Energy Quantification In Human Milk Microcapillaries.

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Machine Learning-Driven Computer Vision System for Automated Fat and Energy Quantification in Human Milk

Lujan E Huamanga-Chumbes1, Erwin J Sacoto-Cabrera2, Jaime Lloret3

  • 1TESLA Laboratory, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco 08003, Peru.

Sensors (Basel, Switzerland)
|March 28, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a low-cost computer vision system for accurate human milk fat quantification. It significantly reduces measurement error compared to traditional methods, aiding neonatal nutrition assessment.

Keywords:
clinical informaticscomputer vision systemhuman milkimage segmentationmachine learningneonatal nutritionuncertainty analysis

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Area of Science:

  • Biomedical Engineering
  • Nutritional Science
  • Machine Learning

Background:

  • Accurate lipid quantification in human milk is crucial for neonatal health.
  • Traditional methods like creamatocrit have limitations including bias and uncertainty.
  • Developing automated, precise, and cost-effective quantification methods is needed.

Purpose of the Study:

  • To develop and validate a low-cost Computer Vision System (CVS) for estimating cream fraction (c) in human milk.
  • To utilize machine learning regression for deriving fat and energy content from the estimated cream fraction.
  • To provide a reagent-free and operationally feasible alternative for neonatal nutritional assessment.

Main Methods:

  • A Computer Vision System optimized for Gold-LED spectrum was developed.
  • Machine Learning (ML) regression models were evaluated across Gray Scale, RGB, and Combined feature spaces.
  • Rational Quadratic Gaussian Process Regression (GPR) was identified as the optimal model.
  • SHAP analysis was used to interpret model feature importance.

    • Main Results:

    • The Rational Quadratic GPR model achieved a high predictive stability with R2=0.867.
    • The CVS demonstrated a 57.5% reduction in relative error compared to manual benchmarks.
    • SHAP analysis highlighted the importance of Red channel intensities and Blue contrast gradients for lipid globule optical scattering.

    Conclusions:

    • The developed Computer Vision System is a stable, non-invasive sensing modality for human milk lipid quantification.
    • This cost-effective computational framework offers a precise alternative for nutritional assessment in neonatal intensive care units and milk banks.