A fully deep learning model for the automatic identification of cephalometric landmarks
김용현, 이채나, 하은규, 최윤정, 한상선,
소속 상세정보
김용현 ( Kim Young-Hyun ) - Yonsei University College of Dentistry Department of Oral and Maxillofacial Radiology
이채나 ( Lee Che-Na ) - Yonsei University College of Dentistry Department of Oral and Maxillofacial Radiology
하은규 ( Ha Eun-Gyu ) - Yonsei University College of Dentistry Department of Oral and Maxillofacial Radiology
최윤정 ( Choi Yoon-Jeong ) - Yonsei University College of Dentistry Department of Orthodontics
한상선 ( Han Sang-Sun ) - Yonsei University College of Dentistry Department of Oral and Maxillofacial Radiology
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.
키워드
Anatomic Landmarks; Artificial Intelligence; Dental Digital Radiography; Deep Learning; Neural Network Models
원문 및 링크아웃 정보
등재저널 정보