Learning Cephalometric Landmarks for Diagnostic Features Using Regression Trees

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...

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Bibliographic Details
Main Authors: Sameera Suhail, Kayla Harris, Gaurav Sinha, Maayan Schmidt, Sujala Durgekar, Shivam Mehta, Madhur Upadhyay
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Bioengineering
Subjects:
cephalograms
anatomical landmarks
machine learning
regression trees
orthodontics
Online Access:https://www.mdpi.com/2306-5354/9/11/617

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https://www.mdpi.com/2306-5354/9/11/617

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