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

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.

Keywords: Anatomic Landmarks; Artificial Intelligence; Deep Learning; Dental Digital Radiography; Neural Network Models.

Fig. 1. Cephalometric identification of the 13 landmarks used in this study. S: sella, N: nasion, Or: orbitale, Po: porion, A: A-point, B: B-point, Pog: pogonion, Me: menton, UIB: upper incisor border, LIB: lower incisor border, PNS: posterior nasal spine, ANS: anterior nasal spine, Ar: articulare.

Fig. 2. The structure of the proposed fully automatic landmark detection model using a convolutional neural network (CNN). A. The overall workflow of the 2-step machines of the proposed model. B. The structure of the CNN model. ROI: region of interest; PNS: posterior nasal spine; ELU: exponential linear units.

Fig. 3. Comparison of the predicted (cross line) and reference (dot) locations of 13 landmarks. S: sella, N: nasion, Or: orbitale, Po: porion, A: A-point, B: B-point, Pog: pogonion, Me: menton, UIB: upper incisor border, LIB: lower incisor border, PNS: posterior nasal spine, ANS: anterior nasal spine, Ar: articulare.

Fig. 4. Comparison of the mean radial errors of expert variability and predicted results. S: sella, N: nasion, Or: orbitale, Po: porion, A: A-point, B: B-point, Pog: pogonion, Me: menton, UIB: upper incisor border, LIB: lower incisor border, PNS: posterior nasal spine, ANS: anterior nasal spine, Ar: articulare.