Comparative Evaluation of Conventional and OnyxCeph™ Dental Software Measurements on Cephalometric Radiography

Digital versus Manual Tracing in Cephalometric Analysis: A Systematic Review and Meta-Analysis

Background: Over the years, various researchers have attempted to compare digital cephalometry with the conventional manual approach. There is a need to comprehensively analyze the findings from the earlier studies and determine the potential advantages and limitations of each method. The present systematic review aimed to compare the accuracy of digital and manual tracing in cephalometric analysis for the identification of skeletal and dental landmarks. Methods: A systematic search was performed using the keywords "Digital" AND "Manual" AND "Cephalometry" to identify relevant studies published in the English language in the past decade. The electronic data resources consulted for the elaborate search included the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, CINAHL, EMBASE, PsycINFO, Scopus, ERIC, and ScienceDirect with controlled vocabulary and free text terms. Results: A total of n = 20 studies were identified that fulfilled the inclusion and exclusion criteria within the timeframe of 2013 to 2023. The data extracted from the included articles and corresponding meta-analyses are presented in the text. Conclusions: The findings of the present systematic review and meta-analysis revealed trends suggesting that digital tracing may offer reliable measurements for specific cephalometric parameters efficiently and accurately. Orthodontists must consider the potential benefits of digital cephalometry, including time-saving and user-friendliness.

Preciseness of artificial intelligence for lateral cephalometric measurements

BACKGROUND

The aim of the study was to assess the accuracy and efficiency of a new artificial intelligence (AI) method in performing lateral cephalometric radiographic measurements.

MATERIALS AND METHODS

A total of 200 lateral cephalometric radiographs were assessed for quality and included. Three methods were used to perform the cephalometric measurements: (1) the AI method using WebCeph software (AssembleCircle Corp., Gyeonggi-do, Republic of Korea), (2) the modified AI method using WebCeph software after manual modification of the landmarks' position, and (3) using OnyxCeph software (Image Instruments GmbH, Chemnitz, Germany) by manual landmark identification and digital measurements generation. The results of the measurements produced by the three methods were compared, in addition to comparing the time required for the measurements' generation required for each method.

RESULTS

Statistically significant differences were detected between the measurements resulting from the three used methods. Fewer differences were detected between the modified AI method and the OnyxCeph method. The AI method produced the measurements the fastest followed by the modified AI method and then the OnyxCeph method.

CONCLUSIONS

Considering the used AI software, AI followed by manual tuning of the landmarks' position might be an accurate method in lateral cephalometric analysis. AI alone is still not fully reliable at locating the different landmarks on the lateral cephalometric radiographs.

Intelligent quantitative assessment of skeletal maturation based on multi-stage model: a retrospective cone-beam CT study of cervical vertebrae

OBJECTIVE

To develop new logistic regression estimative models of the cervical vertebral maturation index (CVMI) based on cone-beam CT (CBCT)-derived parameters for intelligent evaluating skeletal maturation.

METHODS

From 231 CBCT volumes (age range 7-17, mean age 11.09 years), 154 were randomly selected to produce 2D sagittal projections of the second to fourth cervical vertebrae (C2-C4). From 19 quantitative parameters, significant predictors were deduced to formulate logistic models. Using the CVMI and significant predictors of 77 other subjects, performance of the models was externally examined by direct comparison and the area under the receiver operating characteristic curve (AUC). Models were modified if required, to improve their accuracy.

RESULTS

Chronological age, C3 height ([Formula: see text], and ratio of posterior height to lower width of C4 [Formula: see text] were entered as significant predictors. Accuracy of the models was acceptable (total AUC = 0.91) except for 4th and 5th stage (AUC of 0.82 and 0.83, respectively), which were mis-predicted inversely. Adjusted models were generated by bivariate logistic regression analysis and adding significant parameters ([Formula: see text] and [Formula: see text], with odds ratios of 3.308 and 3.38, respectively) from 58 subjects in 4th and 5th stages of CVMI in the model establishment group. The total AUC increased to 0.94, along with an increase in the accuracy of the latter optimized models to 77.9 and 87%, respectively.

CONCLUSION

The new intelligent models reliably estimated skeletal maturation and can be utilized in the clinical field or machine learning-based skeletal maturation assessment.