Identification of ‘Point A’ as the prevalent source of error in cephalometric analysis of lateral radiographs

Grogger, P.; Sacher, C.; Weber, Samuel P.; Millesi, Gabriele; Seemann, Rudolf

doi:10.1016/j.ijom.2018.03.019

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Thirdly, similar to other dental studies based on cephalograms, we had to manually mark relevant landmarks on cephalograms to construct the reference test [38]. The maximal convexity or deepest concavity on the contour were difficult to identify, which might be the reason why the localization deviation of A' was relatively large [39].…”

Section: Discussionmentioning

confidence: 99%

Automated Adenoid Hypertrophy Assessment with Lateral Cephalometry in Children Based on Artificial Intelligence

et al. 2021

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Adenoid hypertrophy may lead to pediatric obstructive sleep apnea and mouth breathing. The routine screening of adenoid hypertrophy in dental practice is helpful for preventing relevant craniofacial and systemic consequences. The purpose of this study was to develop an automated assessment tool for adenoid hypertrophy based on artificial intelligence. A clinical dataset containing 581 lateral cephalograms was used to train the convolutional neural network (CNN). According to Fujioka’s method for adenoid hypertrophy assessment, the regions of interest were defined with four keypoint landmarks. The adenoid ratio based on the four landmarks was used for adenoid hypertrophy assessment. Another dataset consisting of 160 patients’ lateral cephalograms were used for evaluating the performance of the network. Diagnostic performance was evaluated with statistical analysis. The developed system exhibited high sensitivity (0.906, 95% confidence interval [CI]: 0.750–0.980), specificity (0.938, 95% CI: 0.881–0.973) and accuracy (0.919, 95% CI: 0.877–0.961) for adenoid hypertrophy assessment. The area under the receiver operating characteristic curve was 0.987 (95% CI: 0.974–1.000). These results indicated the proposed assessment system is able to assess AH accurately. The CNN-incorporated system showed high accuracy and stability in the detection of adenoid hypertrophy from children’ lateral cephalograms, implying the feasibility of automated adenoid hypertrophy screening utilizing a deep neural network model.

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Section: Discussionmentioning

confidence: 99%

Automated Adenoid Hypertrophy Assessment with Lateral Cephalometry in Children Based on Artificial Intelligence

et al. 2021

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“…Using a superposition of several cephalometric techniques, Lenza et al [ 20 ] concluded that both Steiner and Ricketts analyses present similar accuracy when doing the tracing, which contrasts with our results, because we observed the greatest differences between their results. Grogger et al [ 21 ] showed that landmarks and the cephalometric tracings of true lines can generate the most errors when determining sagittal positions or skeletal inclinations. Therefore, standardization and a regular definition of the landmarks and planes to be used are the most important factors for obtaining comparable results between patients.…”

Section: Discussionmentioning

confidence: 99%

The Airway Volume Related to the Maxillo-Mandibular Position Using 3D Analysis

Ravelo

Olate

Muñoz

et al. 2021

BioMed Research International

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Objective. The aim of this research was to compare three cephalometric analyses and their correlation with the airway volume in subjects with different skeletal classes using 2D and 3D images. Study Design. Cross-sectional descriptive study. Material and Method. Steiner, McNamara, and Ricketts analyses and the airway volume were compared in 115 subjects who were candidates for orthognathic surgery under diagnosis using cone beam computed tomography (CBCT); 46 males (40%) and 69 females (60%) were included. The sagittal positions of the maxilla and mandible, the angulation of the mandibular plane, the sagittal positions of the upper and lower incisors, measurements of the largest or shortest airway area, and the volume were compared using Spearman’s test considering a p value < 0.05. Results. Differences were observed between the Steiner and McNamara techniques for the sagittal position of the maxilla ( p = 0.01 ). For mandibular angulation, there was a greater difference between values for Steiner and Ricketts techniques ( p = 0.001 ). In the upper incisor, the results for McNamara and Ricketts techniques were significantly different ( p = 0.004 ). Analysing the airway, subjects with a class II skeletal pattern had a smaller volume than those with a class III pattern ( p = 0.034 ). Conclusion. It may be concluded that skeletal class II patients have a significantly smaller airway volume than class III patients. The skeletal parameter does not always relate to the airway volume; however, the high mandibular angle could be related to the airway conditions.

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Automatic analysis of lateral cephalograms based on high-resolution net

Chang

Wang

et al. 2023

American Journal of Orthodontics and Dentofacial Orthopedics

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Identification of ‘Point A’ as the prevalent source of error in cephalometric analysis of lateral radiographs

Cited by 3 publications

References 25 publications

Automated Adenoid Hypertrophy Assessment with Lateral Cephalometry in Children Based on Artificial Intelligence

Automated Adenoid Hypertrophy Assessment with Lateral Cephalometry in Children Based on Artificial Intelligence

The Airway Volume Related to the Maxillo-Mandibular Position Using 3D Analysis

Automatic analysis of lateral cephalograms based on high-resolution net

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