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Related Experiment Video

Updated: Oct 10, 2025

State of the Art Cranial Ultrasound Imaging in Neonates
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Asymmetric Three-dimensional Convolutions For Preterm Infants' Pose Estimation.

Lucia Migliorelli, Daniele Berardini, Francesca Rossini

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary

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    Medical & biological engineering & computing·2025

    We developed a deep learning framework for monitoring preterm infants

    Area of Science:

    • Medical technology
    • Computer vision
    • Artificial intelligence

    Background:

    • Computer-assisted tools can aid in monitoring preterm infants' movements in the neonatal intensive care unit (NICU).
    • Effective monitoring can help clinicians identify potential preterm-birth complications.
    • Current research often prioritizes accuracy over efficiency in model development.

    Purpose of the Study:

    • To propose a deep learning framework for accurate and efficient pose estimation of preterm infants.
    • To optimize the training and prediction phases of pose estimation models.
    • To achieve a balance between model reliability and computational efficiency.

    Main Methods:

    • A two-convolutional neural network (CNN) pipeline was developed for pose estimation.

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    Related Experiment Videos

    Last Updated: Oct 10, 2025

    State of the Art Cranial Ultrasound Imaging in Neonates
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    Quantifying Intermembrane Distances with Serial Image Dilations
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  • The first CNN provides initial joint position predictions.
  • The second CNN, Asy-regression CNN, refines predictions using asymmetric convolutions for temporal optimization.

    • Main Results:

    • The Asy-regression CNN reduced training and prediction times by 66% compared to its counterpart.
    • The root mean square error remained unchanged, indicating maintained accuracy.
    • The framework prioritizes time-efficiency without compromising reliability.

    Conclusions:

    • The proposed deep learning framework offers an efficient solution for preterm infant pose estimation.
    • Asymmetric convolutions significantly reduce computational time.
    • This approach supports the development of sustainable monitoring technologies in the NICU.