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Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts

Authors
 Hwangyu Lee  ;  Jung Min Cho  ;  Susie Ryu  ;  Seungmin Ryu  ;  Euijune Chang  ;  Young-Soo Jung  ;  Jun-Young Kim 
Citation
 SCIENTIFIC REPORTS, Vol.13(1) : 15506, 2023-09 
Journal Title
SCIENTIFIC REPORTS
Issue Date
2023-09
MeSH
Algorithms ; Artificial Intelligence* ; Cephalometry ; Deep Learning* ; Humans ; Reproducibility of Results
Abstract
This study aimed to propose a fully automatic posteroanterior (PA) cephalometric landmark identification model using deep learning algorithms and compare its accuracy and reliability with those of expert human examiners. In total, 1032 PA cephalometric images were used for model training and validation. Two human expert examiners independently and manually identified 19 landmarks on 82 test set images. Similarly, the constructed artificial intelligence (AI) algorithm automatically identified the landmarks on the images. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the performance of the model. The performance of the model was comparable with that of the examiners. The MRE of the model was 1.87 ± 1.53 mm, and the SDR was 34.7%, 67.5%, and 91.5% within error ranges of < 1.0, < 2.0, and < 4.0 mm, respectively. The sphenoid points and mastoid processes had the lowest MRE and highest SDR in auto-identification; the condyle points had the highest MRE and lowest SDR. Comparable with human examiners, the fully automatic PA cephalometric landmark identification model showed promising accuracy and reliability and can help clinicians perform cephalometric analysis more efficiently while saving time and effort. Future advancements in AI could further improve the model accuracy and efficiency.

© 2023. Springer Nature Limited.
Files in This Item:
T202305953.pdf Download
DOI
10.1038/s41598-023-42870-z
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Surgery (구강악안면외과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Jun-Young(김준영) ORCID logo https://orcid.org/0000-0002-6596-6135
Jung, Young Soo(정영수) ORCID logo https://orcid.org/0000-0001-5831-6508
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/196588
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