Convolutional mesh autoencoders for the 3-dimensional identification of FGFR-related craniosynostosis

Clinical diagnosis of craniofacial anomalies requires expert knowledge. Recent studies have shown that artificial intelligence (AI) based facial analysis can match the diagnostic capabilities of expert clinicians in syndrome identification. In general, these systems use 2D images and analyse texture...

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Veröffentlicht in:	Scientific reports Jg. 12; H. 1; S. 2230 - 8
Hauptverfasser:	O’ Sullivan, Eimear, van de Lande, Lara S., Papaioannou, Athanasios, Breakey, Richard W. F., Jeelani, N. Owase, Ponniah, Allan, Duncan, Christian, Schievano, Silvia, Khonsari, Roman H., Zafeiriou, Stefanos, Dunaway, David. J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 09.02.2022 Nature Publishing Group Nature Portfolio
Schlagworte:	639/705 692/308 692/700 Artificial Intelligence Computed tomography Computer Simulation Cranial sutures Craniofacial syndromes Craniosynostoses - classification Craniosynostoses - diagnosis Craniosynostoses - diagnostic imaging Craniosynostosis Crouzon syndrome Diagnosis Face - abnormalities Fibroblast growth factor receptors Head - abnormalities Humanities and Social Sciences Humans Image processing Image Processing, Computer-Assisted - methods Imaging, Three-Dimensional - methods Infant Magnetic resonance imaging Medical imaging multidisciplinary Photography Science Science (multidisciplinary) Tomography, X-Ray Computed Topography
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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