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Related Experiment Video

Updated: Mar 15, 2026

Analysis of Craniomaxillofacial Malformations in Mice Using Three-dimensional Microcomputed Tomography
02:42

Analysis of Craniomaxillofacial Malformations in Mice Using Three-dimensional Microcomputed Tomography

Published on: January 17, 2025

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Smartphone 3D Scanning Technology and 3D Semi-Synthetic Data for Processing Infant Head Deformities Using Artificial

Omar C Quispe-Enriquez1, José Luis Lerma1

  • 1Photogrammetry and Laser Scanner Research Group (GIFLE), Department of Cartographic Engineering, Geodesy and Photogrammetry, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a smartphone 3D scanning method for diagnosing infant head deformities like plagiocephaly. Machine learning models accurately classify these conditions using morphometric data, offering a non-invasive, low-cost diagnostic tool.

Keywords:
3D point clouds processingartificial intelligenceinfant cranial deformitiesmachine learningmobile phonesemi-synthetic data

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Area of Science:

  • Medical technology
  • Artificial Intelligence
  • Biomedical engineering

Background:

  • Accurate, non-invasive methods are crucial for early detection of infant cranial deformities.
  • Common deformities include plagiocephaly, brachycephaly, dolichocephaly, turricephaly, and trigonocephaly.

Purpose of the Study:

  • To develop and validate a 3D scanning smartphone application for classifying infant head deformities.
  • To assess the efficacy of machine learning models in automated deformity detection.

Main Methods:

  • Utilized a 3D scanning smartphone app to generate head point clouds.
  • Expanded a dataset of 60 3D scans to 3600 semi-synthetic scans.
  • Extracted 138 morphometric descriptors and trained decision tree, random forest, and multilayer perceptron models.

Main Results:

  • Machine learning models achieved high classification accuracy, with F1-scores reaching 0.98.
  • Demonstrated the effectiveness of using morphometric descriptors for deformity classification.
  • Validated the approach on both real and semi-synthetic 3D head data.

Conclusions:

  • Combining mobile 3D sensing, AI, and semi-synthetic data offers a promising approach for clinical decision support.
  • Low-cost, portable optical sensors can be effectively utilized for infant head deformity assessment.
  • The developed methodology shows potential for predictive support in clinical settings.