A prospective study of soft- and hard-tissue changes after mandibular advancement surgery: Midline changes in the chin area

Digital Surgical Guides in Mandibular Genioplasty: Evaluating Precision Against Conventional Techniques

BACKGROUND

Mandibular genioplasty, a central procedure in oral and maxillofacial surgery, has traditionally relied on surgeon experience with potential limitations in precision. The advent of digital methods, particularly computer-aided design/computer-aided manufacturing (CAD/CAM), offers a promising alternative. This study aims to evaluate the efficacy of digital surgical guides in improving the precision of mandibular genioplasty.

METHODS

A prospective analysis of 50 patients undergoing genioplasty was performed, 30 in the experimental group using digital surgical guides and 20 in the control group using traditional methods. Three-dimensional reconstructions were obtained using cone-beam computed tomography (CBCT) and digital scans. Osteotomy guides were 3D-printed based on group assignment. Postoperatively, accuracy was assessed by measuring distances between landmarks.

RESULTS

The experimental group showed significantly reduced horizontal positioning errors in genioplasty advancement, with no significant differences in vertical errors. For genioplasty retraction, the experimental group showed fewer vertical positioning errors, while horizontal errors remained consistent.

CONCLUSIONS

The use of digital surgical guides in mandibular genioplasty significantly improves surgical accuracy, resulting in improved outcomes and patient satisfaction. This study highlights the potential of digital methods in refining oral and maxillofacial surgical procedures.

LEVEL OF EVIDENCE III

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery

Application of deep learning (DL) and machine learning (ML) is rapidly increasing in the medical field. DL is gaining significance for medical image analysis, particularly, in oral and maxillofacial surgeries. Owing to the ability to accurately identify and categorize both diseased and normal soft- and hard-tissue structures, DL has high application potential in the diagnosis and treatment of tumors and in orthognathic surgeries. Moreover, DL and ML can be used to develop prediction models that can aid surgeons to assess prognosis by analyzing the patient's medical history, imaging data, and surgical records, develop more effective treatment strategies, select appropriate surgical modalities, and evaluate the risk of postoperative complications. Such prediction models can play a crucial role in the selection of treatment strategies for oral and maxillofacial surgeries. Their practical application can improve the utilization of medical staff, increase the treatment accuracy and efficiency, reduce surgical risks, and provide an enhanced treatment experience to patients. However, DL and ML face limitations, such as data drift, unstable model results, and vulnerable social trust. With the advancement of social concepts and technologies, the use of these models in oral and maxillofacial surgery is anticipated to become more comprehensive and extensive.

Three-dimensional analysis of hard and soft tissue changes in skeletal class II patients with high mandibular plane angle undergoing surgery

This study aimed to study 3-dimensional (3D) changes of hard and soft tissues of skeletal class II patients after 2-jaw surgery and genioplasty. 32 adult patients diagnosed with mandibular hypoplasia who underwent 2-jaw surgery of maxillary impaction, mandibular advancement and genioplasty were enrolled. Cone-beam computed tomography and 3D stereophotogrammetry was conducted 1 week before and 6 months after surgery. Dolphin imaging software was used to establish a 3D digitizing model and 3D measurement system. Paired t-test was performed to compare the values before and after surgery. Pearson's correlation test assessed the degree of correlations between hard and soft tissue change. The mean impaction of the maxilla was 2.600 ± 3.088 mm at A. The mean advancement of the mandible was 7.806 ± 2.647 mm at B. There was a significant upward and forward movement for most landmarks of the nose and lip, while a significant decrease in nasal tip height (lateral view), upper lip height, and upper and lower vermilion height. The nose's width was significantly increased. For maxillary, Sn, Ac-r, Ac-l, and Ls demonstrated a significant correlation with A and U1 in the anteroposterior axis. However, there were no significant correlations among them in the vertical axis. For mandibular, Li demonstrated a significant correlation with L1 in the anteroposterior axis specifically for the mandible. Notably, correlations between the landmarks of the chin's hard and soft tissues were observed across all axes. The utilization of 3-D analysis facilitated a quantitative comprehension of both hard and soft tissues, thereby furnishing valuable insights for the strategic formulation of orthognathic treatment plans targeting patients with skeletal class II conditions.

Development and accuracy of artificial intelligence-generated prediction of facial changes in orthodontic treatment: a scoping review

Artificial intelligence (AI) has been utilized in soft-tissue analysis and prediction in orthodontic treatment planning, although its reliability has not been systematically assessed. This scoping review was conducted to outline the development of AI in terms of predicting soft-tissue changes after orthodontic treatment, as well as to comprehensively evaluate its prediction accuracy. Six electronic databases (PubMed, EBSCOhost, Web of Science, Embase, Cochrane Library, and Scopus) were searched up to March 14, 2023. Clinical studies investigating the performance of AI-based systems in predicting post-orthodontic soft-tissue alterations were included. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Joanna Briggs Institute (JBI) appraisal checklist for diagnostic test accuracy studies were applied to assess risk of bias, while the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) assessment was conducted to evaluate the certainty of outcomes. After screening 2500 studies, four non-randomized clinical trials were finally included for full-text evaluation. We found a low level of evidence indicating an estimated high overall accuracy of AI-generated prediction, whereas the lower lip and chin seemed to be the least predictable regions. Furthermore, the facial morphology simulated by AI via the fusion of multimodality images was considered to be reasonably true. Since all of the included studies that were not randomized clinical trials (non-RCTs) showed a moderate to high risk of bias, more well-designed clinical trials with sufficient sample size are needed in future work.

Three-dimensional virtual planning in mandibular advancement surgery: Soft tissue prediction based on deep learning

The study aimed at developing a deep-learning (DL)-based algorithm to predict the virtual soft tissue profile after mandibular advancement surgery, and to compare its accuracy with the mass tensor model (MTM). Subjects who underwent mandibular advancement surgery were enrolled and divided into a training group and a test group. The DL model was trained using 3D photographs and CBCT data based on surgically achieved mandibular displacements (training group). Soft tissue simulations generated by DL and MTM based on the actual surgical jaw movements (test group) were compared with soft-tissue profiles on postoperative 3D photographs using distance mapping in terms of mean absolute error in the lower face, lower lip, and chin regions. 133 subjects were included - 119 in the training group and 14 in the test group. The mean absolute error for DL-based simulations of the lower face region was 1.0 ± 0.6 mm and was significantly lower (p = 0.02) compared with MTM-based simulations (1.5 ± 0.5 mm). CONCLUSION: The DL-based algorithm can predict 3D soft tissue profiles following mandibular advancement surgery. With a clinically acceptable mean absolute error. Therefore, it seems to be a relevant option for soft tissue prediction in orthognathic surgery. Therefore, it seems to be a relevant options.

Effects of different surgical techniques and displacement distances on the soft tissue profile via orthodontic-orthognathic treatment of class II and class III malocclusions

Background

Orthognathic surgery can be carried out using isolated mandibular or maxillary movement and bimaxillary procedures. In cases of moderate skeletal malocclusion, camouflage treatment by premolar extraction is another treatment option. All these surgical procedures can have a different impact on the soft tissue profile.

Methods

The changes in the soft tissue profile of 187 patients (Class II: 53, Class III: 134) were investigated. The treatment approaches were differentiated as follows: Class II: mandible advancement (MnA), bimaxillary surgery (MxS/MnA), upper extraction (UpEX), or Class III: maxillary advancement (MxA), mandible setback (MnS), bimaxillary surgery (MxA/MnS), and lower extraction (LowEX) as well as the extent of skeletal deviation (moderate Wits appraisal: − 7 mm to 7 mm, pronounced: Wits <− 7 mm, > 7 mm, respectively). This resulted in five groups for Class II treatment and seven groups for Class III treatment.

Results

In the Class II patients, a statistically significant difference (p ≤ 0.05) between UpEX and moderate MnA was found for facial profile (N′-Prn-Pog’), soft tissue profile (N′-Sn-Pog’), and mentolabial angle (Pog’-B′-Li). In the Class III patients, a statistically significant differences (p ≤ 0.05) occurred between LowEX and moderate MxA for facial profile (N′-Prn-Pog’), soft tissue profile (N′-Sn-Pog’), upper and lower lip distacne to esthetic line (Ls/Li-E-line), and lower lip length (Sto-Gn’). Only isolated significant differences (p < 0.05) were recognized between the moderate surgical Class II and III treatments as well between the pronounced Class III surgeries. No statistical differences were noticed between moderate and pronounced orthognathic surgery.

Conclusions

When surgery is required, the influence of orthognathic surgical techniques on the profile seems to be less significant. However, it must be carefully considered if orthognathic or camouflage treatment should be done in moderate malocclusions as a moderate mandibular advancement in Class II therapy will straighten the soft tissue profile much more by increasing the facial and soft tissue profile angle and reducing the mentolabial angle than camouflage treatment. In contrast, moderate maxillary advancement in Class III therapy led to a significantly more convex facial and soft tissue profile by decreasing distances of the lips to the E-Line as well as the lower lip length.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13005-021-00264-4.