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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Vydané v:	Journal of cranio-maxillo-facial surgery Ročník 52; číslo 10; s. 1172 - 1187
Hlavní autori:	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.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Scotland Elsevier Ltd 01.10.2024
Predmet:	Artificial intelligence Dysmorphology Machine learning Syndromic craniosynostosis Dysmorphology Syndromic craniosynostosis Artificial intelligence Machine learning
ISSN:	1010-5182, 1878-4119, 1878-4119
On-line prístup:	Získať plný text
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