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Open-source, machine and deep learning-based automated algorithm for gestational age estimation through smartphone

Arjun D Desai1,2, Chunlei Peng1,3, Leyuan Fang1

  • 1Department of Biomedical Engineering, Duke University, Durham 27708, USA.

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Summary
This summary is machine-generated.

Accurately estimate premature infant gestational age using smartphone imaging of anterior lens capsule vasculature (ALCV). This automated algorithm offers a novel, accessible tool for neonatal care, especially in low-resource settings.

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Area of Science:

  • Neonatal Medicine
  • Ophthalmology
  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Accurate gestational age estimation is vital for neonatal care and determining prematurity.
  • Current methods may be invasive or inaccessible in resource-limited settings.
  • Anterior lens capsule vasculature (ALCV) offers a potential biomarker for gestational age.

Purpose of the Study:

  • To develop and validate a fully automated algorithm for estimating gestational age in premature infants.
  • To utilize smartphone-based imaging of ALCV for non-invasive gestational age assessment.
  • To provide an accessible tool for remote and point-of-care neonatal assessments.

Main Methods:

  • A fully automated algorithm employing a fully convolutional network for image segmentation.
  • Extraction of ALCV features using a residual neural network architecture.
  • Classification of gestational age using a support vector machine trained on extracted features.
  • Validation via leave-one-out cross-validation on videos from 124 neonates.

Main Results:

  • The algorithm successfully segments usable anterior capsule regions.
  • ALCV features are effectively extracted and classified for gestational age estimation.
  • Leave-one-out cross-validation demonstrates the algorithm's classification performance.
  • The developed software is made open source.

Conclusions:

  • A novel, automated algorithm for gestational age estimation using smartphone ALCV imaging has been developed.
  • This technology shows promise for accurate and accessible neonatal assessment, particularly in low-income countries.
  • The open-source nature of the software facilitates widespread adoption and further research.