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A fully deep learning model for the automatic identification of cephalometric landmarks

Authors
 Young Hyun Kim  ;  Chena Lee  ;  Eun-Gyu Ha  ;  Yoon Jeong Choi  ;  Sang-Sun Han 
Citation
 IMAGING SCIENCE IN DENTISTRY, Vol.51(3) : 299-306, 2021-09 
Journal Title
IMAGING SCIENCE IN DENTISTRY
ISSN
 2233-7822 
Issue Date
2021-09
Keywords
Anatomic Landmarks ; Artificial Intelligence ; Deep Learning ; Dental Digital Radiography ; Neural Network Models
Abstract
Purpose: This study aimed to propose a fully automatic landmark identification model based on a deep learning algorithm using real clinical data and to verify its accuracy considering inter-examiner variability.

Materials and methods: In total, 950 lateral cephalometric images from Yonsei Dental Hospital were used. Two calibrated examiners manually identified the 13 most important landmarks to set as references. The proposed deep learning model has a 2-step structure-a region of interest machine and a detection machine-each consisting of 8 convolution layers, 5 pooling layers, and 2 fully connected layers. The distance errors of detection between 2 examiners were used as a clinically acceptable range for performance evaluation.

Results: The 13 landmarks were automatically detected using the proposed model. Inter-examiner agreement for all landmarks indicated excellent reliability based on the 95% confidence interval. The average clinically acceptable range for all 13 landmarks was 1.24 mm. The mean radial error between the reference values assigned by 1 expert and the proposed model was 1.84 mm, exhibiting a successful detection rate of 36.1%. The A-point, the incisal tip of the maxillary and mandibular incisors, and ANS showed lower mean radial error than the calibrated expert variability.

Conclusion: This experiment demonstrated that the proposed deep learning model can perform fully automatic identification of cephalometric landmarks and achieve better results than examiners for some landmarks. It is meaningful to consider between-examiner variability for clinical applicability when evaluating the performance of deep learning methods in cephalometric landmark identification.
Files in This Item:
T202125308.pdf Download
DOI
10.5624/isd.20210077
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Radiology (영상치의학교실) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Orthodontics (교정과학교실) > 1. Journal Papers
Yonsei Authors
Lee, Chena(이채나) ORCID logo https://orcid.org/0000-0002-8943-4192
Choi, Yoon Jeong(최윤정) ORCID logo https://orcid.org/0000-0003-0781-8836
Han, Sang Sun(한상선) ORCID logo https://orcid.org/0000-0003-1775-7862
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/187785
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