Validity and reliability of a new edge-based computerized method for identification of cephalometric landmarks

Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform

Background

Artificial Intelligence has created a huge impact in different areas of dentistry. Automated cephalometric analysis is one of the major applications of artificial intelligence in the field of orthodontics. Various automated cephalometric software have been developed which utilizes artificial intelligence and claim to be reliable. The purpose of this study was to compare the linear and angular cephalometric measurements obtained from web-based fully automated Artificial Intelligence (AI) driven platform “WebCeph”™ with that from manual tracing and evaluate the validity and reliability of automated cephalometric measurements obtained from “WebCeph”™.

Methods

Thirty pre-treatment lateral cephalograms of patients were randomly selected. For manual tracing, digital images of same cephalograms were printed using compatible X-ray printer. After calibration, a total of 18 landmarks was plotted and 12 measurements (8 angular and 4 linear) were obtained using standard protocols. The digital images of each cephalogram were uploaded to “WebCeph”™ server. After image calibration, the automated cephalometric measurements obtained through AI digitization were downloaded for each image. Intraclass correlation coefficient (ICC) was used to determine agreement between the measurements obtained from two methods. ICC value < 0.75 was considered as poor to moderate agreement while an ICC value between 0.75 and 0.90 was considered as good agreement. Agreement was rated as excellent when ICC value > 0.90 was obtained.

Results

All the measurements had ICC value above 0.75. A higher ICC value > 0.9 was obtained for seven parameters i.e. ANB, FMA, IMPA/L1 to MP (°), LL to E-line, L1 to NB (mm), L1 to NB (°), S-N to Go-Gn whereas five parameters i.e. UL to E-line, U1 to NA (mm), SNA, SNB, U1 to NA (°) showed ICC value between 0.75 and 0.90.

Conclusion

A good agreement was found between the cephalometric measurements obtained from “WebCeph”™ and manual tracing.

Automated identification of cephalometric landmarks: Part 1-Comparisons between the latest deep-learning methods YOLOV3 and SSD

OBJECTIVE

To compare the accuracy and computational efficiency of two of the latest deep-learning algorithms for automatic identification of cephalometric landmarks.

MATERIALS AND METHODS

A total of 1028 cephalometric radiographic images were selected as learning data that trained You-Only-Look-Once version 3 (YOLOv3) and Single Shot Multibox Detector (SSD) methods. The number of target labeling was 80 landmarks. After the deep-learning process, the algorithms were tested using a new test data set composed of 283 images. Accuracy was determined by measuring the point-to-point error and success detection rate and was visualized by drawing scattergrams. The computational time of both algorithms was also recorded.

RESULTS

The YOLOv3 algorithm outperformed SSD in accuracy for 38 of 80 landmarks. The other 42 of 80 landmarks did not show a statistically significant difference between YOLOv3 and SSD. Error plots of YOLOv3 showed not only a smaller error range but also a more isotropic tendency. The mean computational time spent per image was 0.05 seconds and 2.89 seconds for YOLOv3 and SSD, respectively. YOLOv3 showed approximately 5% higher accuracy compared with the top benchmarks in the literature.

CONCLUSIONS

Between the two latest deep-learning methods applied, YOLOv3 seemed to be more promising as a fully automated cephalometric landmark identification system for use in clinical practice.

Comparison of manual, digital and lateral CBCT cephalometric analyses

OBJECTIVE

The aim of this study was to compare the reliability of three different methods of cephalometric analysis.

MATERIAL AND METHODS

Conventional pretreatment lateral cephalograms and cone beam computed tomography (CBCT) scans from 50 subjects from a radiological clinic were selected in order to test the three methods: manual tracings (MT), digitized lateral cephalograms (DLC), and lateral cephalograms from CBCT (LC-CBCT). The lateral cephalograms were manually analyzed through the Dolphin Imaging 11.0™ software. Twenty measurements were performed under the same conditions, and retraced after a 30-day period. Paired t tests and the Dahlberg formula were used to evaluate the intra-examiner errors. The Pearson's correlation coefficient and one-way analysis of variance (ANOVA) tests were used to compare the differences between the methods.

RESULTS

Intra-examiner reliability occurred for all methods for most of the measurements. Only six measurements were different between the methods and an agreement was observed in the analyses among the 3 methods.

CONCLUSIONS

The results demonstrated that all evaluated methodologies are reliable and valid for scientific research, however, the method used in the lateral cephalograms from the CBCT proved the most reliable.