Accuracy of artificial intelligence-assisted growth prediction in skeletal Class I preadolescent patients using serial lateral cephalograms for a 2-year growth interval.

A Larkin Jun-Sik Kim Namkug Kim Seung-Hak Baek S Yamada K Park K Tai Yoshinobu Yanagi Jae Hyun Park

Published in: Orthodontics & craniofacial research (2024)

Despite the possibility of using the CNN model to predict growth, further studies are needed to improve the prediction accuracy in HTLs and STLs of the chin area.

Keyphrases

artificial intelligence
end stage renal disease
machine learning
big data
ejection fraction
chronic kidney disease
deep learning
newly diagnosed
prognostic factors
peritoneal dialysis
convolutional neural network