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...

Celý popis

Podrobná bibliografie

Vydáno v:	Journal of cranio-maxillo-facial surgery Ročník 52; číslo 10; s. 1172 - 1187
Hlavní autoři:	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:	angličtina
Vydáno:	Scotland Elsevier Ltd 01.10.2024
Témata:	Artificial intelligence Dysmorphology Machine learning Syndromic craniosynostosis Dysmorphology Syndromic craniosynostosis Artificial intelligence Machine learning
ISSN:	1010-5182, 1878-4119, 1878-4119
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!