A more accurate soft-tissue prediction model for Class III 2-jaw surgeries

Self Cite

128

112

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.

Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

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

Hwang¹,

Park²,

Moon³

et al. 2019

Self Cite

128

112

“…[1][2][3][4] Correction of skeletal Class III malocclusions is the most frequent reason to seek an orthognathic surgery consultation. 2,5 With increasing demand for improved facial esthetics and advances in surgical techniques, clinicians must be aware of changing trends in the management of severe Class III malocclusion patients.…”

Section: Introductionmentioning

confidence: 99%

“…For skeletal Class III treatment, the combination of two-jaw surgery with an additional genioplasty has become a common surgical procedure. 5,[8][9][10][11] In recent years, an increase in the proportion of two-jaw surgeries has also been perceived at the author's institution.…”

Section: Introductionmentioning

confidence: 99%

Modern trends in Class III orthognathic treatment: A time series analysis

Lee¹,

Park²,

Lee

2016

Self Cite

Objective: To examine the current trends in surgical-orthodontic treatment for patients with Class III malocclusion using time-series analysis. Materials and Methods: The records of 2994 consecutive patients who underwent orthognathic surgery from January 1, 2004, through December 31, 2015, at Seoul National University Dental Hospital, Seoul, Korea, were reviewed. Clinical data from each surgical and orthodontic treatment record included patient's sex, age at the time of surgery, malocclusion classification, type of orthognathic surgical procedure, place where the orthodontic treatment was performed, orthodontic treatment modality, and time elapsed for pre-and postoperative orthodontic treatment. Results: Out of the orthognathic surgery patients, 86% had Class III malocclusion. Among them, two-jaw surgeries have become by far the most common orthognathic surgical treatment these days. The age at the time of surgery and the number of new patients had seasonal variations, which demonstrated opposing patterns. There was neither positive nor negative correlation between preand postoperative orthodontic treatment time. Elapsed orthodontic treatment time for both before and after Class III orthognathic surgeries has been decreasing over the years. Conclusion: Results of the time series analysis might provide clinicians with some insights into current surgical and orthodontic management. (Angle Orthod. 2017;87:269-278)

“…For example, there is no single prediction algorithm that can be applied to various surgical movements simultaneously. Surgical movements differ for Class II and Class III skeletal discrepancies, and prior studies [1][2][3][4][5][6][7] derived a different prediction equation for each skeletal discrepancy. Prediction results for bimaxillary surgery were reported 8,9 to be less accurate than those for single-jaw surgeries.…”

Section: Introductionmentioning

confidence: 99%

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

Suh

Lee

et al. 2019

Self Cite

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.