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Detecting 3D syndromic faces as outliers using unsupervised normalizing flow models.

Jordan J Bannister1, Matthias Wilms2, J David Aponte3

  • 1Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.

Artificial Intelligence in Medicine
|December 3, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an AI method using 3D facial scans to detect genetic syndromes by identifying unusual facial features in unaffected individuals. This approach offers a new, unsupervised tool for early syndrome screening and diagnosis.

Keywords:
3D facial shapeComputer-assisted diagnosisGenetic syndromeNormalizing flowOutlier detection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Genetics

Background:

  • Distinctive facial features are common in genetic syndromes.
  • Current computer-assisted diagnosis methods often rely on supervised learning, requiring extensive syndromic facial image datasets.
  • Collecting large, comprehensive datasets for training is challenging.

Purpose of the Study:

  • To develop an unsupervised method for detecting genetic syndromes using 3D facial scans.
  • To identify syndromic faces as statistical outliers within a population of unaffected individuals.
  • To provide an interpretable AI model for clinical use.

Main Methods:

  • Utilized unsupervised, normalizing flow-based manifold and density estimation models.
  • Trained models exclusively on 3D facial surface scans of unaffected subjects.
  • Developed a gradient-based interpretability mechanism for model explanations.

Main Results:

  • The best flow-based model achieved an ROC-AUC of 86.3% on a diverse dataset.
  • Outperformed other unsupervised comparison methods in detecting syndromic faces.
  • Demonstrated improved performance over existing outlier scores for 3D face-based syndrome detection.

Conclusions:

  • Unsupervised, outlier-based screening tools are viable for genetic syndrome detection.
  • The proposed methods generalize and enhance previous approaches for 3D face-based syndrome identification.
  • This AI approach offers a promising avenue for early syndrome detection and diagnosis.