Development and validation of predictive models for skeletal malocclusion classification using airway and cephalometric landmarks

Abstract Objective This study aimed to develop a deep learning model to predict skeletal malocclusions with an acceptable level of accuracy using airway and cephalometric landmark values obtained from analyzing different CBCT images. Background In orthodontics, multitudinous studies have reported th...

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Principais autores:	Anand Marya, Samroeng Inglam, Nattapon Chantarapanich, Sujin Wanchat, Horn Rithvitou, Prasitthichai Naronglerdrit
Formato:	Artigo
Idioma:	Inglês
Publicado:	BMC 2024-09-01
Series:	BMC Oral Health
Assuntos:	Orthodontics Airway Skeletal CBCT Cephalometric Machine Learning
Acceso en liña:	https://doi.org/10.1186/s12903-024-04779-5
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