Accuracy of posteroanterior cephalogram landmarks and measurements identification using a cascaded convolutional neural network algorithm: A multicenter study

Objective

: To quantify the effects of midline-related landmark identification on midline deviation measurements in posteroanterior (PA) cephalograms using a cascaded convolutional neural network (CNN).

Methods

: A total of 2,903 PA cephalogram images obtained from 9 university hospitals were divided into training, internal validation, and test sets (n = 2,150, 376, and 377). As the gold standard, 2 orthodontic professors marked the bilateral landmarks, including the frontozygomatic suture point and latero-orbitale (LO), and the midline landmarks, including the crista galli, anterior nasal spine (ANS), upper dental midpoint (UDM), lower dental midpoint (LDM), and menton (Me). For the test, Examiner-1 and Examiner-2 (3-year and 1-year orthodontic residents) and the Cascaded-CNN models marked the landmarks. After point-to-point errors of landmark identification, the successful detection rate (SDR) and distance and direction of the midline landmark deviation from the midsagittal line (ANS-mid, UDM-mid, LDM-mid, and Me-mid) were measured, and statistical analysis was performed.

Results

: The cascaded-CNN algorithm showed a clinically acceptable level of point-to-point error (1.26 mm vs. 1.57 mm in Examiner-1 and 1.75 mm in Examiner-2). The average SDR within the 2 mm range was 83.2%, with high accuracy at the LO (right, 96.9%; left, 97.1%), and UDM (96.9%). The absolute measurement errors were less than 1 mm for ANS-mid, UDM-mid, and LDM-mid compared with the gold standard.

Conclusions

: The cascaded-CNN model may be considered an effective tool for the auto-identification of midline landmarks and quantification of midline deviation in PA cephalograms of adult patients, regardless of variations in the image acquisition method.

Landmark annotation and mandibular lateral deviation analysis of posteroanterior cephalograms using a convolutional neural network

Background/purpose

Facial asymmetry is relatively common in the general population. Here, we propose a fully automated annotation system that supports analysis of mandibular deviation and detection of facial asymmetry in posteroanterior (PA) cephalograms by means of a deep learning-based convolutional neural network (CNN) algorithm.

Materials and methods

In this retrospective study, 400 PA cephalograms were collected from the medical records of patients aged 4 years 2 months-80 years 3 months (mean age, 17 years 10 months; 255 female patients and 145 male patients). A deep CNN with two optimizers and a random forest algorithm were trained using 320 PA cephalograms; in these images, four PA landmarks were independently identified and manually annotated by two orthodontists.

Results

The CNN algorithms had a high coefficient of determination (R ² ), compared with the random forest algorithm (CNN-stochastic gradient descent, R ²  = 0.715; CNN-Adam, R ²  = 0.700; random forest, R ²  = 0.486). Analysis of the best and worst performances of the algorithms for each landmark demonstrated that the right latero-orbital landmark was most difficult to detect accurately by using the CNN. Based on the annotated landmarks, reference lines were defined using an algorithm coded in Python. The CNN and random forest algorithms exhibited similar accuracy for the distance between the menton and vertical reference line.

Conclusion

Our findings imply that the proposed deep CNN algorithm for detection of facial asymmetry may enable prompt assessment and reduce the effort involved in orthodontic diagnosis.