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AI-based diagnosis and phenotype - Genotype correlations in syndromic craniosynostoses.

Quentin Hennocq1, Giovanna Paternoster2, Corinne Collet3

  • 1Imagine Institute, INSERM UMR1163, 75015, Paris, France; Département de chirurgie maxillo-faciale et chirurgie plastique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, 75015, Paris, France; Laboratoire 'Forme et Croissance du Crâne', Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Faculté de Médecine, Université Paris Cité, Paris, France.

Journal of Cranio-Maxillo-Facial Surgery : Official Publication of the European Association for Cranio-Maxillo-Facial Surgery
|August 26, 2024
PubMed
Summary

This study developed an AI model to diagnose syndromic craniosynostoses using facial photographs. The artificial intelligence approach achieved 70.2% accuracy in identifying these complex genetic conditions.

Keywords:
Artificial intelligenceDysmorphologyMachine learningSyndromic craniosynostosis

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Genetics

Background:

  • Syndromic craniosynostoses, including Apert, Crouzon, Muenke, Pfeiffer, and Saethre Chotzen syndromes, are frequently diagnosed genetic disorders.
  • Accurate and timely diagnosis is crucial for effective management and treatment planning.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI)-based model for the automated diagnosis of syndromic craniosynostoses using 2D facial photographs.
  • To investigate genotype/phenotype correlations in specific syndromic craniosynostoses, namely Apert, Crouzon, and Pfeiffer syndromes.

Main Methods:

  • A deep learning model was trained on frontal and lateral facial photographs from 541 genetically diagnosed patients (1979-2023).
  • Geometric and textural features were extracted and classified using XGboost (eXtreme Gradient Boosting).
  • The model's performance was evaluated on an independent international validation set.

Main Results:

  • The AI model correctly diagnosed 70.2% of patients in the validation set (p < 0.001).
  • A specific FGFR2 genotype in Crouzon-Pfeiffer syndrome was associated with a milder clinical presentation.
  • The study demonstrates the potential of AI in identifying syndromic craniosynostoses.

Conclusions:

  • AI-powered analysis of facial photographs offers a promising new method for the automatic detection of syndromic craniosynostoses.
  • Further research can refine AI diagnostic accuracy and explore genotype-phenotype relationships in these conditions.