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Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

Merve Erkınay Özdemir1, Ziya Telatar2, Osman Eroğul3

  • 1Department of Electrical-Electronics Engineering, Faculty of Engineering and Natural Sciences, Iskenderun Technical University, Iskenderun, Turkey. merve.erkinayozdemir@iste.edu.tr.

Australasian Physical & Engineering Sciences in Medicine
|May 3, 2018
PubMed
Summary

This study presents an automated system for diagnosing dysmorphic syndromes using facial malformations. The hierarchical decision tree achieved 86.7% accuracy, outperforming clinical experts.

Keywords:
Artificial neural networkClassificationDysmorphic syndromeHierarchical decision treePre diagnosis

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Area of Science:

  • Medical imaging analysis
  • Computational biology
  • Syndrome identification

Background:

  • Facial malformations are key indicators for early diagnosis of dysmorphic syndromes.
  • Accurate recognition of these features aids in differential diagnosis and face recognition.

Purpose of the Study:

  • To automatically classify specific dysmorphic syndromes (Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn) and healthy controls.
  • To develop a non-invasive, accurate, and automated system for dysmorphic syndrome pre-diagnosis.

Main Methods:

  • Facial images were analyzed by marking reference points and calculating distance ratios.
  • A neural network-based hierarchical decision tree was developed for classification.
  • Performance was compared against k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers, and a clinical expert.

Main Results:

  • The hierarchical decision tree achieved a classification accuracy of 86.7%.
  • ANN and k-NN achieved accuracies of 73% and 50%, respectively.
  • The automated system's accuracy surpassed that of a clinical expert (46.7%).

Conclusions:

  • The developed automated system demonstrates high accuracy in recognizing dysmorphic syndromes from simple imaging data.
  • The method is independent of patient age, sex, and race, offering a versatile diagnostic tool.
  • This approach shows potential for pre-diagnosis support for clinical experts.