Automatic Cephalometric Analysis Research Articles

Evaluation of the accuracy of fully automatic cephalometric analysis software with artificial intelligence algorithm.

The aim of this study is to evaluate whether fully automatic cephalometric analysis software with artificial intelligence algorithms is as accurate as non-automated cephalometric analysis software for clinical diagnosis and research. This is a retrospective archive study using lateral cephalometric radiographs taken from individuals aged 12-20 years. Cephalometric measurement data were obtained from these lateral cephalometric radiographs by manual landmark marking with non-automated computer software (Dolphin 11.8). Again, the same radiographs were made using fully automatic digital cephalometric analysis software OrthoDx™ (AI-Powered Orthodontic Imaging System, Phimentum) and WebCeph (Assemblecircle, Seoul, Korea) with artificial intelligence algorithm, without manual intervention of the researcher and fully automatic markings and measurements were made by the software. According to the consistency test, a statistically significant good level of consistency was found between Dolphin and OrthoDx™ measurements and Dolphin and WebCeph measurements in angular measurements (ICC > 0.75, P < .01, ICC > 0.75, P < 0, respectively. 01). A weak level of consistency was found in linear measurement and soft tissue parameters in both software (ICC < 0.50, P < .05, ICC < 0.50, P < .05), and the difference between measurements was statistically found to be different from "0." The results obtained from fully automatic cephalometric analysis software with artificial intelligence algorithms are similar to the results of non-automated cephalometric analysis software, although there are differences in some parameters. To minimize the margin of error in artificial intelligence-based fully automatic cephalometric software, the manual intervention of the observer is needed.

Automatic analysis of lateral cephalograms based on high-resolution net

Cephalometric analysis is essential in orthodontic treatment, and it is progressing toward automatic cephalometric analysis. This study aimed to establish a cephalometric landmark detection model on the basis of a high-resolution net and improve the accuracy with high resolution. A total of 2000 lateral cephalograms were collected to construct a dataset, and the number of target landmarks was 51. A high-resolution network model was applied to the landmark detection task. Four models were trained by adjusting different input resolutions to choose the most suitable resolution. A test set consisting of 300 lateral cephalograms was used for evaluation. The model was evaluated from the error size and distribution of each landmark. After 200 epochs of training, a landmark detection model was established. Under different resolutions of the input image, the mean model radial error decreased initially and then increased. At 680 × 920 pixels resolution, the minimum error and the highest detection success rate were obtained. The mean radial error was 1.08 ± 0.87 mm. The detection success rates of 2.0 mm, 2.5 mm, 3.0 mm, and 4.0 mm were 89.00%, 94.00%, 96.33%, and 98.67%, respectively. The mean radial errors of 22 landmarks were<1 mm, and the errors of other landmarks were<2 mm except for the pterion. The error distribution of landmarks followed a certain pattern. An automatic landmark detection model based on a high-resolution net was established to recognize 51 landmarks. The model showed high detection accuracy, which provides a basis for further measurement application.

Artificial intelligence system for automated landmark localization and analysis of cephalometry.

Cephalometric analysis is essential for diagnosis, treatment planning and outcome assessment of orthodontics and orthognathic surgery. Utilizing artificial intelligence (AI) to achieve automated landmark localization has proved feasible and convenient. However, current systems remain insufficient for clinical application, as patients exhibit various malocclusions in cephalograms produced by different manufacturers while limited cephalograms were applied to train AI in these systems. A robust and clinically applicable AI system was proposed for automatic cephalometric analysis. First, 9870 cephalograms taken by different radiography machines with various malocclusions of patients were collected from 20 medical institutions. Then 30 landmarks of all these cephalogram samples were manually annotated to train an AI system, composed of a two-stage convolutional neural network and a software-as-a-service system. Further, more than 100 orthodontists participated to refine the AI-output landmark localizations and retrain this system. The average landmark prediction error of this system was as low as 0.94 ± 0.74 mm and the system achieved an average classification accuracy of 89.33%. An automatic cephalometric analysis system based on convolutional neural network was proposed, which can realize automatic landmark location and cephalometric measurements classification. This system showed promise in improving diagnostic efficiency in clinical circumstances.

Comparison between cephalometric measurements using digital manual and web-based artificial intelligence cephalometric tracing software.

ABSTRACTObjective: The aim of this study was to compare the measurements performed with digital manual (DM) cephalometric analysis and automatic cephalometric analysis obtained from an online artificial intelligence (AI) platform, according to different sagittal skeletal malocclusions.Methods: Cephalometric radiographs of 105 randomly selected individuals (mean age: 17.25 ± 1.87 years) were included in this study. Dolphin Imaging software was used for DM cephalometric analysis, and the WebCeph platform was used for AI-based cephalometric analysis. In total, 10 linear and 12 angular measurements were evaluated. The paired t-test, one-way ANOVA test, and intraclass correlation coefficient tests were used to evaluate the differences between the two methods. The level of statistical significance was set at p< 0.05.Results: Except for SNB, NPog, U1.SN, U1.NA, L1-APog, I/I, and LLE parameters, all other parameters presented significant differences between the two methods (p< 0.05). While there was no difference (p> 0.05) in both SNA and SNB measurements between the two methods in the Class I malocclusion group, there was a difference between both methods in the Class II malocclusion group. Meanwhile, only the SNA in the Class III malocclusion group was different (p< 0.05). The ANB angle differed significantly in all three malocclusion groups. For both methods, all parameters except CoA and CoGn were found to have good correlation.Conclusion: Although significant differences were detected in some measurements between the two cephalometric analysis methods, not all differences were clinically significant. The AI-based cephalometric analysis method needs to be developed for more specific malocclusions.

Comparison of cephalometric measurements between conventional and automatic cephalometric analysis using convolutional neural network

ObjectiveThe rapid development of artificial intelligence technologies for medical imaging has recently enabled automatic identification of anatomical landmarks on radiographs. The purpose of this study was to compare the results of an automatic cephalometric analysis using convolutional neural network with those obtained by a conventional cephalometric approach.Material and methodsCephalometric measurements of lateral cephalograms from 35 patients were obtained using an automatic program and a conventional program. Fifteen skeletal cephalometric measurements, nine dental cephalometric measurements, and two soft tissue cephalometric measurements obtained by the two methods were compared using paired t test and Bland-Altman plots.ResultsA comparison between the measurements from the automatic and conventional cephalometric analyses in terms of the paired t test confirmed that the saddle angle, linear measurements of maxillary incisor to NA line, and mandibular incisor to NB line showed statistically significant differences. All measurements were within the limits of agreement based on the Bland-Altman plots. The widths of limits of agreement were wider in dental measurements than those in the skeletal measurements.ConclusionsAutomatic cephalometric analyses based on convolutional neural network may offer clinically acceptable diagnostic performance. Careful consideration and additional manual adjustment are needed for dental measurements regarding tooth structures for higher accuracy and better performance.

Automatic cephalometric analysis: is it time to switch to a hands-free method?

Aim: To evaluate the reliability of the automatic cephalometric analysis in relation to the semi-automatic method. Methods: Fifty lateral cephalometric radiographs were selected and two dental surgeons performed the Steiner and Tweed analyses independently using the semi-automatic method on the Radiocef Studio 2® software suite (Radiomemory, Belo Horizonte, MG, Brazil), and the automatic method on the Kodak Dental Imaging Software (Carestream Health, Rochester, NY, USA). After thirty days, 30% of the sample was re-evaluated to assess intra-observer agreement. Ten angular and linear measurements of both analyses were selected, averaged for both observers and compared using Student's t-test with a significance level of 5% (α=0.05). Intra and inter-observer agreement were assessed through Intraclass Correlation Coefficient. Results: Intra-observer reproducibility was excellent for all measurements and inter-observer reproducibility was excellent for most of them. Significant differences (p&lt;0.05) were found between automatic and semi-automatic methods for all measurements. Most of the measurements were significantly higher (p&lt;0.05) with the automatic method. Conclusion: Semi-automatic cephalometric analysis can not be replaced with a completely automatic method.

Accuracy of Automatic Cephalometric Software on Landmark Identification

This study was to assess the accuracy of an automatic cephalometric analysis software in the identification of cephalometric landmarks. Thirty randomly selected digital lateral cephalograms of patients undergoing orthodontic treatment were used in this study. Thirteen landmarks (S, N, Or, A-point, U1T, U1A, B-point, Gn, Pog, Me, Go, L1T, and L1A) were identified on the digital image by an automatic cephalometric software and on cephalometric tracing by manual method. Superimposition of printed image and manual tracing was done by registration at the soft tissue profiles. The accuracy of landmarks located by the automatic method was compared with that of the manually identified landmarks by measuring the mean differences of distances of each landmark on the Cartesian plane where X and Y coordination axes passed through the center of ear rod. One-Sample T test was used to evaluate the mean differences. Statistically significant mean differences (p<0.05) were found in 5 landmarks (Or, A-point, Me, L1T, and L1A) in horizontal direction and 7 landmarks (Or, A-point, U1T, U1A, B-point, Me, and L1A) in vertical direction. Four landmarks (Or, A-point, Me, and L1A) showed significant (p<0.05) mean differences in both horizontal and vertical directions. Small mean differences (<0.5mm) were found for S, N, B-point, Gn, and Pog in horizontal direction and N, Gn, Me, and L1T in vertical direction. Large mean differences were found for A-point (3.0 < 3.5mm) in horizontal direction and L1A (>4mm) in vertical direction. Only 5 of 13 landmarks (38.46%; S, N, Gn, Pog, and Go) showed no significant mean difference between the automatic and manual landmarking methods. It is concluded that if this automatic cephalometric analysis software is used for orthodontic diagnosis, the orthodontist must correct or modify the position of landmarks in order to increase the accuracy of cephalometric analysis.

Assessment of the Reliability of Automatic Cephalometric Analysis Software

Assessment of the Reliability of Automatic Cephalometric Analysis Software

Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization

Cephalometric images usually have low contrast. The existing techniques for automatic cephalometric analysis usually use histogram equalization for image enhancement. This technique has the advantage of being fully automatic and nonlinear. However, it suffers from spikes, excessive enhancement, and lack of brightness preservation. The proposed technique is an adaptive histogram equalization technique that uses wavelet based gradient histograms. This paper compares its performance with two traditional techniques, three histogram modification based techniques, and two wavelet based techniques. Forty digital and scanned cephalograms are used to conduct tests. In addition to visual histograms and intensity profiles, the proposed method is compared in terms of eight quantitative measures. The various measures are applied to analyze the results in terms of contrast enhancement (EME, CNR), brightness preservation (AMBE), edge conservation and enhancement (H, TEN), preservation of image structures and non-addition distortion (MSSIM, SVD-M). The proposed method gives good contrast enhancement, with better brightness preservation without losing edge information and with the minimum addition of distortions to the enhanced cephalometric images.

An Evaluation of Cellular Neural Networks for the Automatic Identification of Cephalometric Landmarks on Digital Images

Several efforts have been made to completely automate cephalometric analysis by automatic landmark search. However, accuracy obtained was worse than manual identification in every study. The analogue-to-digital conversion of X-ray has been claimed to be the main problem. Therefore the aim of this investigation was to evaluate the accuracy of the Cellular Neural Networks approach for automatic location of cephalometric landmarks on softcopy of direct digital cephalometric X-rays. Forty-one, direct-digital lateral cephalometric radiographs were obtained by a Siemens Orthophos DS Ceph and were used in this study and 10 landmarks (N, A Point, Ba, Po, Pt, B Point, Pg, PM, UIE, LIE) were the object of automatic landmark identification. The mean errors and standard deviations from the best estimate of cephalometric points were calculated for each landmark. Differences in the mean errors of automatic and manual landmarking were compared with a 1-way analysis of variance. The analyses indicated that the differences were very small, and they were found at most within 0.59 mm. Furthermore, only few of these differences were statistically significant, but differences were so small to be in most instances clinically meaningless. Therefore the use of X-ray files with respect to scanned X-ray improved landmark accuracy of automatic detection. Investigations on softcopy of digital cephalometric X-rays, to search more landmarks in order to enable a complete automatic cephalometric analysis, are strongly encouraged.

Automatic computerized radiographic identification of cephalometric landmarks

Computerized cephalometric analysis currently requires manual identification of landmark locations. This process is time-consuming and limited in accuracy. The purpose of this study was to develop and test a novel method for automatic computer identification of cephalometric landmarks. Spatial spectroscopy (SS) is a computerized method that identifies image structure on the basis of a convolution of the image with a set of filters followed by a decision method using statistical pattern recognition techniques. By this method, characteristic features are used to recognize anatomic structures. This study compared manual identification on a computer monitor and the SS automatic method for landmark identification on minimum resolution images (0.16 cm 2 per pixel). Minimum resolution (defined as the lowest resolution at which a cephalometric structure could be identified) was used to reduce computational time and memory requirements during this development stage of the SS method. Fifteen landmarks were selected on a set of 14 test images. The results showed no statistical difference ( p > 0.05) in mean landmark identification errors between manual identification on the computer display and automatic identification using SS. We conclude that SS shows potential for the automatic detection of landmarks, which is an important step in the development of a completely automatic cephalometric analysis. (Am J Orthod Dentofacial Orthop 1998;113:173–9)

Computerised planning of maxillo-facial osteotomies: The program and its clinical applications

A totally computerised program has been developed which undertakes the 2-dimensional planning of a complete maxillo-facial reconstruction using Le Fort I, II & III, Wassmund, Schuchardt, Küfner, Köle, Dingman, Obwegeser and VSS osteotomies with augmentation or reduction genioplasty and rhinoplasty. The program incorporates the calibration of the equipment, initialisation of new discs, a filing system for osteotomy patients, unlimited expansion facilities for storage of files and an automatic cephalometric analysis which is updated at each stage. A suggested operation, which may be altered for aesthetics, is generated spontaneously by the computer. The surgeon or the patient is free to accept or reject the computer's suggestion in part or whole.