Cargando…
Learning Cephalometric Landmarks for Diagnostic Features Using Regression Trees
Lateral cephalograms provide important information regarding dental, skeletal, and soft-tissue parameters that are critical for orthodontic diagnosis and treatment planning. Several machine learning methods have previously been used for the automated localization of diagnostically relevant landmarks...
Autores principales: | Suhail, Sameera, Harris, Kayla, Sinha, Gaurav, Schmidt, Maayan, Durgekar, Sujala, Mehta, Shivam, Upadhyay, Madhur |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687964/ https://www.ncbi.nlm.nih.gov/pubmed/36354530 http://dx.doi.org/10.3390/bioengineering9110617 |
Ejemplares similares
-
Cephalometric landmark detection without X-rays combining coordinate regression and heatmap regression
por: Takahashi, Kaisei, et al.
Publicado: (2023) -
Three-Dimensional Cephalometric Landmarking and Frankfort Horizontal Plane Construction: Reproducibility of Conventional and Novel Landmarks
por: Dot, Gauthier, et al.
Publicado: (2021) -
Median Lingual Foramen, a new midmandibular cephalometric landmark
por: Vandekerckhove, David, et al.
Publicado: (2020) -
Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection
por: Le, Van Nhat Thang, et al.
Publicado: (2022) -
A study on the reproducibility of cephalometric landmarks
when undertaking a three-dimensional (3D) cephalometric analysis
por: Zamora, Natalia, et al.
Publicado: (2012)