The relationship between 3D dentofacial photogrammetry measurements and traditional cephalometric measurements

Castillo, José Carlos; Gianneschi, Grace; Azer, Demyana; Manosudprasit, Amornrut; Haghi, Arshan; Bansal, Neetu; Allareddy, Veerasathpurush; Masoud, Mohamed I.

doi:10.2319/120317-825

Cited by 6 publications

(6 citation statements)

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“…Studies on related topics in various fields have also been gaining more popularity. 3,[11][12][13][14] Although three-dimensional images have gained popularity these days, [15][16][17][18][19] two-dimensional cephalometric analysis is still a vital tool in orthodontic diagnosis and treatment planning since it provides information regarding a patient's skeletal and soft tissue. Currently, computer-assisted cephalometric analysis eliminates human-induced mechanical errors.…”

Section: Discussionmentioning

confidence: 99%

Automated Identification of Cephalometric Landmarks: Part 2- Might It Be Better Than human?

Hwang¹,

Park²,

Moon³

et al. 2019

The Angle Orthodontist

140

112

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Objectives: To compare detection patterns of 80 cephalometric landmarks identified by an automated identification system (AI) based on a recently proposed deep-learning method, the You-Only-Look-Once version 3 (YOLOv3), with those identified by human examiners. Materials and Methods: The YOLOv3 algorithm was implemented with custom modifications and trained on 1028 cephalograms. A total of 80 landmarks comprising two vertical reference points and 46 hard tissue and 32 soft tissue landmarks were identified. On the 283 test images, the same 80 landmarks were identified by AI and human examiners twice. Statistical analyses were conducted to detect whether any significant differences between AI and human examiners existed. Influence of image factors on those differences was also investigated. Results: Upon repeated trials, AI always detected identical positions on each landmark, while the human intraexaminer variability of repeated manual detections demonstrated a detection error of 0.97 ± 1.03 mm. The mean detection error between AI and human was 1.46 ± 2.97 mm. The mean difference between human examiners was 1.50 ± 1.48 mm. In general, comparisons in the detection errors between AI and human examiners were less than 0.9 mm, which did not seem to be clinically significant. Conclusions: AI showed as accurate an identification of cephalometric landmarks as did human examiners. AI might be a viable option for repeatedly identifying multiple cephalometric landmarks.

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Section: Discussionmentioning

confidence: 99%

Automated Identification of Cephalometric Landmarks: Part 2- Might It Be Better Than human?

Hwang¹,

Park²,

Moon³

et al. 2019

The Angle Orthodontist

140

112

View full text Add to dashboard Cite

show abstract

“…All rights reserved Beam Computed tomography (CBCT) scans 14 or to cut down the application of lateral cephalograms. 15 Although several methods have been proposed to record NHP in 3D, 11 only one study 16 has reported on the stability of NHP in all three dimensions. Additionally, one study has reported the stability of NHP for rotations around the x -and z-axes.…”

Section: Accepted Articlementioning

confidence: 99%

Reproducibility of Natural Head Position assessed with stereophotogrammetry

Jakobsone

Vuollo

Pirttiniemi

2019

Orthod Craniofacial Res

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Objective. The aim of this study was to assess the stability of Natural Head Position (NHP) over time using the 3dMDface System. Setting and sample population. This was an experimental study. Three-dimensional facial images of 40 students were captured on two different occasions, with an interval of at least two weeks. Materials and Methods. The images were taken using a stereophotogrammetric device (3dMD, Atlanta, Georgia). The mirror positioned NHP was obtained in a standing position and then replicated in a sitting position for capturing. The selfbalanced NHP was taken in a sitting position. Rapidform 3D software was used for position angle calculations. The angle changes between the positions were calculated for rotations around the x-, y-and z-axes. Results. The differences between NHP in the self-balanced and mirror positions recorded on the first and second occasions were 2.43 and 1.75 degrees, respectively, around the x-axis. The average changes in NHP around the x-axis between the self-balanced and mirror balanced positions exceeded 3 degrees at the two-week interval. The differences were smaller for the Accepted Article This article is protected by copyright. All rights reserved rotations around the y-and z-axes. Some subjects consistently tended to hold their heads in a more extended position when self-balanced, while others did this when mirror balanced. There was no difference in the reproducibility of NHP between men and women. Conclusion. The reproducibility of NHP for consecutive stereophotogrammetric captures is generally acceptable. The reproducibility of NHP using the mirror position was slightly better compared with NHP in the sitting self-balanced position.

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“…The future goal might be the application of this method to threedimensional (3D) data. While 3D photogrammetry and 3D cephalometrics look promising in the near future, 21,[24][25][26] one of the major challenges might be that a 3D prediction algorithm will demand more massive data than would be required in a two-dimensional situation. Going to 3D would present even more of a burden on measurement and computational resources.…”

Section: Discussionmentioning

confidence: 99%

Predicting soft tissue changes after orthognathic surgery: The sparse partial least squares method

Suh

Lee

et al. 2019

The Angle Orthodontist

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Objectives: To develop a prediction algorithm for soft tissue changes after orthognathic surgery that would result in accurate predictions (1) regardless of types or complexity of operations and (2) with a minimum number of input variables. Materials and Methods: The subjects consisted of 318 patients who had undergone the surgical correction of Class II or Class III malocclusions. Two multivariate methods—the partial least squares (PLS) and the sparse partial least squares (SPLS) methods—were used to construct prediction equations. While the PLS prediction model included 232 input variables, the SPLS method included a reduced number of variables generated by a handicapping algorithm via the sparsity control. The accuracy between the PLS and SPLS models was compared. Results: There were no significant differences in prediction accuracy depending on surgical movements, the sex of the subjects, or additional surgeries. The predictive performance with a reduced set of 34 input variables chosen using the SPLS method was statistically indistinguishable from the full set of variables with the original PLS prediction model. Conclusions: The prediction method proposed in the present study was accurate for a wide range of orthognathic surgeries. A reduced set of input variables could be selected through the SPLS method while simultaneously maintaining a prediction level that was as accurate as that of the original PLS prediction model.

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The relationship between 3D dentofacial photogrammetry measurements and traditional cephalometric measurements

Cited by 6 publications

References 0 publications

Automated Identification of Cephalometric Landmarks: Part 2- Might It Be Better Than human?

Automated Identification of Cephalometric Landmarks: Part 2- Might It Be Better Than human?

Reproducibility of Natural Head Position assessed with stereophotogrammetry

Predicting soft tissue changes after orthognathic surgery: The sparse partial least squares method

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