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

Apert (AS), Crouzon (CS), Muenke (MS), Pfeiffer (PS), and Saethre Chotzen (SCS) are among the most frequently diagnosed syndromic craniosynostoses. The aims of this study were (1) to train an innovative model using artificial intelligence (AI)–based methods on two-dimensional facial frontal, lateral...

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Veröffentlicht in:	Journal of cranio-maxillo-facial surgery Jg. 52; H. 10; S. 1172 - 1187
Hauptverfasser:	Hennocq, Quentin, Paternoster, Giovanna, Collet, Corinne, Amiel, Jeanne, Bongibault, Thomas, Bouygues, Thomas, Cormier-Daire, Valérie, Douillet, Maxime, Dunaway, David J., Jeelani, Nu Owase, van de Lande, Lara S., Lyonnet, Stanislas, Ong, Juling, Picard, Arnaud, Rickart, Alexander J., Rio, Marlène, Schievano, Silvia, Arnaud, Eric, Garcelon, Nicolas, Khonsari, Roman H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Scotland Elsevier Ltd 01.10.2024
Schlagworte:	Artificial intelligence Dysmorphology Machine learning Syndromic craniosynostosis Dysmorphology Syndromic craniosynostosis Artificial intelligence Machine learning
ISSN:	1010-5182, 1878-4119, 1878-4119
Online-Zugang:	Volltext
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