Assessment of the usefulness of three-dimensional scanner in aesthetic evaluation of post-traumatic rhinoplasty

Unveiling the Impact of Three-Dimensional Technology on Rhinoplasty: A Systematic Review and Meta-analysis

This study aimed to investigate the impact of three-dimensional (3D) technology on preoperative evaluation for rhinoplasty.A systematic search was conducted on Embase, MEDLINE, and Web of Science. Studies that utilized 3D technology in preoperative assessment for rhinoplasty were included. The primary outcome was surgeon and patient satisfaction. The secondary outcomes included nasal function, cost-efficiency, reoperation rate, precision, and surgical time.Twelve studies (595 patients) were included. Surgeons reported higher satisfaction with 3D approaches based on precision and postoperative results. Patients expressed satisfaction with the 3D approaches due to a better understanding of the procedure and the ability to discuss with the surgeons planning the procedure and participating in postoperative design. The 3D approaches demonstrated higher surgeon satisfaction (mean difference -0.13, 95% confidence interval [CI] -0.20 to -0.06, p = 0.0002), particularly in revision cases, and provided higher precision compared with the two-dimensional (2D) approaches. However, 3D technology was more expensive and not cost-efficient. There were no significant differences in reoperation rate (odds ratio 0.16, 95% CI 0.02-1.36, p = 0.09) and surgical time. Postoperative nasal function showed inconsistent results.3D technology offered higher surgeon satisfaction and increased precision compared with 2D imaging. However, 3D imaging was expensive and not cost-efficient. Reoperation rate and surgical time were comparable, while postoperative nasal function outcomes showed inconsistent results.

Simulation and Artificial Intelligence in Rhinoplasty: A Systematic Review

BACKGROUND

Rhinoplasty is one of the most popular cosmetic procedures. The complexity of the nasal structure and the substantial aesthetic and functional impact of the operation make rhinoplasty very challenging. The past few years have witnessed an increasing implementation of artificial intelligence (AI) and simulation systems into plastic surgery practice. This review explores the potential uses of AI and simulation models in rhinoplasty.

METHODS

Five electronic databases were searched: PubMed, CINAHL, EMBASE, Scopus, and Web of Science. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis as our basis of organization.

RESULTS

Several simulation models were described to predict the nasal shape that aesthetically matches the patient's face, indicate the implant size in augmentation rhinoplasty and construct three-dimensional (3D) facial images from two-dimensional images. Machine learning was used to learn surgeons' rhinoplasty styles and accurately simulate the outcomes. Deep learning was used to predict rhinoplasty status accurately and analyze the factors associated with increased facial attractiveness after rhinoplasty. Finally, a deep learning model was used to predict patients' age before and after rhinoplasty proving that the procedure made the patients look younger.

CONCLUSION

3D simulation models and AI models can revolutionalize the practice of functional and aesthetic rhinoplasty. Simulation systems can be beneficial in preoperative planning, intra-operative decision making, and postoperative evaluation. In addition, AI models can be trained to carry out tasks that are either challenging or time-consuming for surgeons.

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 .

Anthropometric Parameters for Nose Evaluation and Nasal Surgery Planning

BACKGROUND

The esthetic characteristics of face and nose are commonly evaluated before rhinoplasty using a completely subjective method, due to the lack of validated and reliable methods for quantifying facial esthetics and for accurate nose treatment planning. The aim of the study was to review the literature to determine and evaluate the points, distances, and angles commonly used in the treatment planning for rhinoplasty.

METHODS

Research based on anthropometric studies of the face and nose, published from 1987 to 2018 was included. Finally, 138 papers were selected after a statistical analysis through a simple random and non-random sample selection, and all papers were evaluated in their entirety.

RESULTS

According to the frequency of citation, 198 points, 336 distances, and 199 angles were listed. The first quartile of each distribution was eliminated, and frequency of more than 25% was selected. A group of 49 points, 77 distances, and 11 angles, were classified according to their anatomical region, that is, bone and soft tissues, was obtained.

CONCLUSIONS

An enormous inhomogeneity and lack of standardized anthropometric measurement system, specifically of the nose, was evident, as the studies were conducted by authors of different origins. According to universally accepted parameters, the importance of a reliable method for nose surgery planning is highlighted.

Postoperative Edema Resolution following Rhinoplasty: A Three-Dimensional Morphometric Assessment

BACKGROUND

The final result of rhinoplasty may be masked for several months after surgery because of postoperative edema; however, no objective evidence supports this time estimate. The purpose of this study was to three-dimensionally quantify the decrease in postsurgical nasal edema following rhinoplasty over the first postoperative year.

METHODS

This was a retrospective, three-dimensional, morphometric study of primary, open rhinoplasty patients. Subjects with at least three postoperative three-dimensional images up to 1 year were included. Patients were excluded for closed or secondary procedures or cleft deformities. Images were assessed using three-dimensional stereophotogrammetry (Vectra) and volumetric analysis (Geomagic). Baseline nasal volume (time 0) occurred at the first postoperative visit at 1 to 2 weeks. All subsequent nasal volume measurements were calculated as a percentage of baseline values. Data points from all patients were pooled, and a six-point moving average was used to create an inverse function line of best fit.

RESULTS

Forty patients were included, with 146 three-dimensional photographs quantified. The equation for the inverse function line of best fit of the six-point moving average was y = 1.484 (1/x) + 0.844 (R = 0.85, p < 0.01). According to this equation, approximately two-thirds of edema resolves within the first month, 95 percent after 6 months, and 97.5 percent after 1 year. A plateau is reached at 84.4 percent of the original postoperative volume.

CONCLUSIONS

This study provides quantitative evidence to predict decrement of rhinoplasty edema with time. Three-dimensional morphometric assessment demonstrated a two-thirds decrease in edema at 1 month, a 95 percent decrease at 6 months, and a 97.5 percent decrease at 1 year.

CLINICAL QUESTION/LEVEL OF EVIDENCE

Therapeutic, IV.