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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Bibliographic Details

Published in:	Journal of cranio-maxillo-facial surgery Vol. 52; no. 10; pp. 1172 - 1187
Main Authors:	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
Language:	English
Published:	Scotland Elsevier Ltd 01.10.2024
Subjects:	Artificial intelligence Dysmorphology Machine learning Syndromic craniosynostosis Dysmorphology Syndromic craniosynostosis Artificial intelligence Machine learning
ISSN:	1010-5182, 1878-4119, 1878-4119
Online Access:	Get full text
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