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Exploring the Use of a Length AI Algorithm to Estimate Children's Length from Smartphone Images in a Real-World

Mei Chien Chua1,2,3,4, Matthew Hadimaja5, Jill Wong5

  • 1Department of Neonatology, KK Women's and Children's Hospital, Singapore, Singapore.

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|November 22, 2024
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Summary

A new AI tool uses smartphone images to measure children's length, offering a convenient alternative to traditional methods. This innovation shows promise for accurate growth monitoring at home and in clinics.

Keywords:
AIalgorithmartificial intelligencechildrencomputer visionheightimaginginfantlengthlength estimationmHealthmeasuremobile healthmobile phoneneonatalnewbornpediatricsmartphonesmartphone images

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Area of Science:

  • Pediatric growth monitoring
  • Artificial Intelligence in healthcare
  • Medical imaging analysis

Background:

  • Accurate length measurement is crucial for monitoring growth in children under 18 months.
  • Traditional length measurement is challenging due to equipment, training, and child cooperation requirements.

Purpose of the Study:

  • To develop a Length Artificial Intelligence (LAI) algorithm for convenient recumbent length estimation from smartphone images.
  • To evaluate the LAI algorithm's performance and suitability for personal and clinical use.

Main Methods:

  • A proof-of-concept study involving healthy children aged 0-18 months.
  • Comparison of LAI algorithm's image-based length estimations against standardized length-board measurements.
  • Evaluation of prediction performance on individual images and participant-averaged bases, alongside user experience questionnaires.

Main Results:

  • The LAI algorithm achieved a high prediction success rate (99.4%) across analyzed photos.
  • Mean absolute errors were 2.47 cm for individual images and 1.77 cm for participant-averaged predictions.
  • High user satisfaction reported by parents and investigators regarding photo capture ease.

Conclusions:

  • The LAI algorithm provides an accessible method for estimating children's length using smartphone images, eliminating the need for specialized equipment or personnel.
  • The algorithm's performance and ease of use suggest potential for parental/caregiver use and clinical applications, approaching general clinic accuracy.
  • The study serves as a proof of concept for the LAI algorithm's utility in personal and clinical settings.