Clinical applicability of automated cephalometric landmark identification: Part II - Number of images needed to re-learn various quality of images.

Journal: Orthodontics & craniofacial research

Published Date: Jul 11, 2021

Abstract

AIM: To estimate the number of cephalograms needed to re-learn for different quality images, when artificial intelligence (AI) systems are introduced in a clinic.

Authors

Chihiro Tanikawa

Graduate School of Dentistry, Osaka University, Suita, Japan.
Ayaka Oka

Graduate School of Dentistry, Osaka University, Suita, Japan.
Jaeyoen Lim

Graduate School of Dentistry, Osaka University, Suita, Japan.
Chonho Lee

Cybermedia Center, Osaka University, Suita, Japan.
Takashi Yamashiro

Graduate School of Dentistry, Osaka University, Suita, Japan.

Keywords

Artificial Intelligence Cephalometry Humans Radiography

External Resources

View on PubMed Access via DOI PubMed (34145974)

Clinical applicability of automated cephalometric landmark identification: Part II - Number of images needed to re-learn various quality of images.

Abstract

Authors

Keywords

External Resources

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