A new 3-D tool for planning plastic surgery

Preoperative three-dimensional simulation of the breast appearance after wide local excision or level one oncoplastic techniques for breast-conserving treatment does not set unrealistic expectations for aesthetic outcome: One-year follow-up of a randomised controlled trial

INTRODUCTION

Simulation of aesthetic outcomes of wide local excision and level one oncoplastic breast conserving treatment (BCT) using 3-dimensional surface imaging (3D-SI) prepares women for their aesthetic outcome. It remains unknown whether women's memory of this information at the one-year follow-up matches their perception of reality or affects the quality of life.

METHODS

With ethical approval, a prospective 3-arm RCT was conducted and it included 3D-simulation, viewing post-operative 2D photographs of other women and standard care. At one-year post-surgery, the participants completed a visual analogue scale (VAS) for the question "How well do you think the information about how your breasts are likely to look after surgery reflects how they actually look today?" and the BCT BREAST-Q module. The Kruskal-Wallis test was used to examine between-group differences at a 5% significance level.

RESULTS

From 2017 to 2019, 117 women completed the primary endpoint of being informed about the aesthetic outcome via verbal description, photographs or simulation. Seventy-eight (74%) of the 106 women who remained eligible attended the one-year follow-up. The standardised preoperative 3D-SI simulation did not affect the patient's perception of the aesthetic outcome compared to standard care or viewing 2D photographs as measured using the VAS (p = 0.40) or BREAST-Q scores for satisfaction with information (p = 0.76), satisfaction with breasts (p = 0.70), and psychosocial wellbeing domains (p = 0.81).

DISCUSSION

Viewing their own 3D-SI standardised simulation did not significantly affect how the participants perceived their aesthetic outcome. In addition, it did not alter the patient-reported satisfaction. These results demonstrated that simulation for wide local excision or level one oncoplastic surgery does not set unrealistic expectations of the aesthetic outcome when used in a preoperative setting.

SYNOPSIS

The use of a non-bespoke three-dimensional simulation of the aesthetic outcome for breast conserving treatment in the preoperative setting does not over-inflate expectations compared to standard care.

An Automatic Framework for Nasal Esthetic Assessment by ResNet Convolutional Neural Network

Nasal base aesthetics is an interesting and challenging issue that attracts the attention of researchers in recent years. With that insight, in this study, we propose a novel automatic framework (AF) for evaluating the nasal base which can be useful to improve the symmetry in rhinoplasty and reconstruction. The introduced AF includes a hybrid model for nasal base landmarks recognition and a combined model for predicting nasal base symmetry. The proposed state-of-the-art nasal base landmark detection model is trained on the nasal base images for comprehensive qualitative and quantitative assessments. Then, the deep convolutional neural networks (CNN) and multi-layer perceptron neural network (MLP) models are integrated by concatenating their last hidden layer to evaluate the nasal base symmetry based on geometry features and tiled images of the nasal base. This study explores the concept of data augmentation by applying the methods motivated via commonly used image augmentation techniques. According to the experimental findings, the results of the AF are closely related to the otolaryngologists' ratings and are useful for preoperative planning, intraoperative decision-making, and postoperative assessment. Furthermore, the visualization indicates that the proposed AF is capable of predicting the nasal base symmetry and capturing asymmetry areas to facilitate semantic predictions. The codes are accessible at https://github.com/AshooriMaryam/Nasal-Aesthetic-Assessment-Deep-learning .

Automating the Standardized Cosmesis and Health Nasal Outcomes Survey Classification with Convolutional Neural Networks

Importance: Currently, the aesthetic appearance and structure of the nose in a rhinoplasty patient is evaluated by a surgeon, without automation. Objective: To compare the assessment of convolutional neural networks (CNNs) (machine learning) and a rhinoplasty surgeon's impression of the nose before rhinoplasty. Methods: Preoperative nasal images were scored using a modified standardized cosmesis and health nasal outcomes survey (SCHNOS) questionnaire. Artificial intelligence (AI) models based on CNNs were developed and trained to classify patient nasal aesthetics into one of five categories, representing even intervals on the SCHNOS scoring scale. The models' performances were benchmarked against expert surgeon evaluation. Results: Two hundred thirty-five preoperative patient images were included in the study. The best-performing AI model achieved 61% accuracy and 0.449 average Matthews Correlation Coefficient on new patients. Conclusions: This pilot study suggests a proof-of-concept for AI to allow an automated patient assessment tool trained on preoperative patient images with a potential utility for counseling rhinoplasty patients.

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 .

Facial Surface Anthropometric Features and Measurements With an Emphasis on Rhinoplasty

BACKGROUND

Facial features and measurements are utilized to analyze patients' faces for various reasons, including surgical planning, scientific communications, patient-surgeon communications, and post-surgery evaluations.

OBJECTIVES

There are numerous descriptions regarding these features and measurements scattered throughout the literature, and the authors did not encounter a current compilation of these parameters in the medical literature.

METHODS

A narrative literature review of the published medical literature for facial measurements used for facial analysis in rhinoplasty was conducted through the electronic databases MEDLINE/PubMed and Google Scholar, along with a citation search.

RESULTS

A total of 61 facial features were identified: 45 points (25 bilateral, 20 unilateral), 5 lines (3 bilateral, 2 unilateral), 8 planes, and 3 areas.A total of 122 measurements were identified: 48 distances (6 bilateral, 42 unilateral), 57 angles (13 bilateral, 44 unilateral), and 17 ratios. Supplemental figures were created to depict all features and measurements utilizing a frontal, lateral, or basal view of the face.

CONCLUSIONS

This paper provides the most comprehensive and current compilation of facial measurements to date. The authors believe this compilation will guide further developments (methodologies and software tools) for analyzing nasal structures and assessing the objective outcomes of facial surgeries, in particular rhinoplasty. Moreover, it will improve communication as a reference for facial measurements of facial surface anthropometry, in particular rhinoplasty.

Three-dimensional simulation of aesthetic outcome from breast-conserving surgery compared with viewing photographs or standard care: randomized clinical trial

INTRODUCTION

Over half of women with surgically managed breast cancer in the UK undergo breast-conserving treatment (BCT). While photographs are shown prior to reconstructive surgery or complex oncoplastic procedures, standard practice prior to breast conservation is to simply describe the likely aesthetic changes. Patients have expressed the desire for more personalized information about likely appearance after surgery. The hypothesis was that viewing a three-dimensional (3D) simulation improves patients' confidence in knowing their likely aesthetic outcome after surgery.

METHODS

A randomized, controlled trial of 117 women planning unilateral BCT was undertaken. The randomization was three-way: standard of care (verbal description alone, control group), viewing two-dimensional (2D) photographs, or viewing a 3D simulation before surgery. The primary endpoint was the comparison between groups' median answer on a visual analogue scale (VAS) for the question administered before surgery: 'How confident are you that you know how your breasts are likely to look after treatment?'

RESULTS

The median VAS in the control group was 5.2 (i.q.r. 2.6-7.8); 8.0 (i.q.r. 5.7-8.7) for 2D photography, and 8.9 (i.q.r. 8.2-9.5) for 3D simulation. There was a significant difference between groups (P < 0.010) with post-hoc pairwise comparisons demonstrating a statistically significant difference between 3D simulation and both standard care and viewing 2D photographs (P < 0.010 and P = 0.012, respectively).

CONCLUSION

This RCT has demonstrated that women who viewed an individualized 3D simulation of likely aesthetic outcome for BCT were more confident going into surgery than those who received standard care or who were shown 2D photographs of other women. The impact on longer-term satisfaction with outcome remains to be determined.Registration number: NCT03250260 (http://www.clinicaltrials.gov).

Digitizing rhinoplasty: a web application with three-dimensional preoperative evaluation to assist rhinoplasty surgeons with surgical planning

PURPOSE

Rhinoplasty is one of the most common and challenging plastic surgery procedures. The results of the operation have a significant impact on the facial appearance. The planning is critical for successful rhinoplasty surgery. In this paper, we present a web application designed for preoperative rhinoplasty surgery planning.

METHODS

The application uses the three-dimensional (3D) model of a patient's face and facilitates marking of an extensive number of facial features and auto-calculation of facial measurements to develop a numerical plan of the surgery. The web application includes definitions, illustrations, and formulas to describe the features and measurements. In addition to the existing measurements, the user can calculate the distance between any two points, the angle between any three points, and the ratio of any two distances. We conducted a survey among experienced rhinoplasty surgeons to get feedback about the web application and to understand their attitude toward utilizing 3D models for preoperative planning.

RESULTS

The web application can be accessed and used through any web browser at digitized-rhinoplasty.com. The web application was utilized in our tests and also by the survey participants. The users successfully marked the facial features on the 3D models and reviewed the auto-calculated measurements. The survey results show that the experienced surgeons who tried the web application found it useful for preoperative planning and they also think that utilizing 3D models is beneficial.

CONCLUSIONS

The web application introduced in this paper helps analyzing the patient's face in details utilizing 3D models and provides numeric outputs to be used in the rhinoplasty operation planning. The experienced rhinoplasty surgeons that participated to our survey agree that the web app would be a beneficial tool for rhinoplasty surgeons. We aim to further improve the web application with more functionality to help surgeons for preoperative planning of rhinoplasty.

Aesthetics of Numerical Proportions in Human Cosmetic Surgery

BACKGROUND

Beauty is a universal phenomenon and debate over what constitutes beauty particularly beauty to human body, has raged since philosophy began. The beauty of individual features depends on "ideal" proportions, and it is suggested that expressing beauty in terms of geometry is possible. Assessment of some used parameters in facial surgeries and harmony of various facial features are essential to surgeon, who requires facial analysis. One of these parameters, is nasolabial angle, in patients undergoing rhinoplasty. This study based on theoretical definitions of beauty and proportions performed the search for the application of this numerical proportions in modern cosmetic surgery.

METHODS

Twenty-three samples [16 (69.5%) female and 7 (30.5%)] male] were enrolled from patients who underwent rhinoplasty, by a single surgeon. The nasolabial angle was measured in these patients from their lateral profile photographs with adobe Photoshop, before and after surgery.

RESULTS

Ideal post-operative angle was 111.54±26.5 degrees from this study and 18.8^◦ increase in male and 14.68^◦ increase in female were seen. There was no significant difference between men and women.

CONCLUSION

Our results showed that an ideal proportion can be very useful and practical to assess patient's preoperative expectations and to evaluate the results after surgery and satisfaction of cosmetic surgery process.

Eigen-disfigurement model for simulating plausible facial disfigurement after reconstructive surgery

Background

Patients with facial cancers can experience disfigurement as they may undergo considerable appearance changes from their illness and its treatment. Individuals with difficulties adjusting to facial cancer are concerned about how others perceive and evaluate their appearance. Therefore, it is important to understand how humans perceive disfigured faces. We describe a new strategy that allows simulation of surgically plausible facial disfigurement on a novel face for elucidating the human perception on facial disfigurement.

Method

Longitudinal 3D facial images of patients (N = 17) with facial disfigurement due to cancer treatment were replicated using a facial mannequin model, by applying Thin-Plate Spline (TPS) warping and linear interpolation on the facial mannequin model in polar coordinates. Principal Component Analysis (PCA) was used to capture longitudinal structural and textural variations found within each patient with facial disfigurement arising from the treatment. We treated such variations as disfigurement. Each disfigurement was smoothly stitched on a healthy face by seeking a Poisson solution to guided interpolation using the gradient of the learned disfigurement as the guidance field vector. The modeling technique was quantitatively evaluated. In addition, panel ratings of experienced medical professionals on the plausibility of simulation were used to evaluate the proposed disfigurement model.

Results

The algorithm reproduced the given face effectively using a facial mannequin model with less than 4.4 mm maximum error for the validation fiducial points that were not used for the processing. Panel ratings of experienced medical professionals on the plausibility of simulation showed that the disfigurement model (especially for peripheral disfigurement) yielded predictions comparable to the real disfigurements.

Conclusions

The modeling technique of this study is able to capture facial disfigurements and its simulation represents plausible outcomes of reconstructive surgery for facial cancers. Thus, our technique can be used to study human perception on facial disfigurement.

Electronic supplementary material

The online version of this article (doi:10.1186/s12880-015-0050-7) contains supplementary material, which is available to authorized users.

Volumetric fibular transfer planning with shape-based indicators in mandibular reconstruction

In preoperative planning for mandibular reconstructive surgery, it is necessary to determine the osteotomy lines for fibular shaping and the proper placement of fibular segments in the mandible. Although virtual surgical planning has been utilized in preoperative decision making, current software designs require manual operation and a trial-and-error process to refine the reconstruction plan. We have developed volumetric fibular transfer simulation software that can quickly design a preoperative plan based on direct volume manipulation and quantitative comparison with the patient's original mandible. We propose three quantitative shape indicators-volume ratio, contour error, and maximum projection-for symmetrical lesions of the mandible, and have implemented a parallel computation algorithm for the semiautomatic placement of fibular segments. Using this virtual planning software, we conducted a retrospective study of the computed tomography data from nine patients. We found that combining direct volume manipulation with real-time local search of placement improved the applicability of the planning system to optimize mandibular reconstruction.

Volumetric surgical planning system for fibular transfer in mandibular reconstruction

This presentation introduces a new software design for virtual preoperative planning for free fibular transfer in mandibular reconstructive surgery. Direct volume resection and manipulation of superimposed fibular segments allow interactive editing of the surgical plan without the need for a surface modeling process. We also introduce three shape indicators: volume ratio, contour error and maximum projection for evaluating the reconstruction plan from geometrical aspects. The indicators significantly quantify the difference between 2-segment and 3-segment cases, and suggest optimization of preoperative planning while satisfying appropriate placement margins for fibular segments.