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Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis.

Guido de Jong1, Elmar Bijlsma2, Jene Meulstee3,4

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|September 19, 2020
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Summary

A deep learning algorithm accurately classified infant head shapes from 3D images, distinguishing craniosynostosis subtypes from healthy controls. This AI tool aids in the swift diagnosis of premature cranial suture fusion in infants.

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Area of Science:

  • Medical imaging
  • Artificial intelligence
  • Pediatric surgery

Background:

  • Craniosynostosis involves premature fusion of infant cranial sutures, impeding brain and skull growth.
  • Early diagnosis of craniosynostosis is crucial for managing cosmetic and functional issues.
  • Accurate classification of craniosynostosis subtypes is essential for appropriate treatment.

Purpose of the Study:

  • To evaluate a deep learning algorithm's ability to classify infant head shapes.
  • To differentiate between healthy controls and three craniosynostosis subtypes: scaphocephaly, trigonocephaly, and anterior plagiocephaly.
  • To assess the accuracy of AI in diagnosing craniosynostosis subtypes using 3D stereophotographs.

Main Methods:

  • Collected 3D stereophotographs of infants with scaphocephaly (n=76), trigonocephaly (n=40), anterior plagiocephaly (n=27), and healthy controls (n=53).
  • Employed a deep learning network to classify cranial shape data.
  • Utilized stratified tenfold cross-validation for training and testing the algorithm.

Main Results:

  • The deep learning algorithm achieved 99.5% accuracy in classifying cranial shapes.
  • 195 out of 196 3D stereophotographs were correctly classified during testing.
  • The AI demonstrated high accuracy in discriminating between craniosynostosis subtypes and healthy controls.

Conclusions:

  • Deep learning algorithms trained on 3D stereophotographs can accurately classify craniosynostosis subtypes.
  • AI-based analysis of cranial shape data offers a promising tool for rapid diagnosis.
  • This technology can significantly aid in identifying infants with craniosynostosis, enabling timely intervention.