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

Topsakal, Oğuzhan; Akbaş, Mustafa İlhan; Demirel, Doga; Núñez, Rafael; Smith, Bria Synae; Perez, Michael Francis; Çelikoyar, M. Mazhar

doi:10.1007/s11548-020-02251-7

Cited by 12 publications

(7 citation statements)

References 22 publications

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“…While these algorithms can be used to compute area and volume on any 3D models, we showed the results and analysis for measuring certain regions on 3D face models. We have developed open-source code implementations of these algorithms, which are accessible on GitHub [7], and have integrated them into a web-based Facial Analysis tool [9], [10]. Our goal is to contribute to the progress of facial analysis, an essential component of pre and post-surgery evaluations.…”

Section: Discussionmentioning

confidence: 99%

“…[8]. For example, we have implemented the area and volume algorithms to enhance the facial analyzer tool that focuses on pre-and post-surgery analysis and evaluations needed for rhinoplasty [9], [10]. As facial plastic surgeries are more digitized and computerized guidance will be provided before, during, and after surgery, the importance of determining the amount of area and volume will increase.…”

Section: Introductionmentioning

confidence: 99%

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Algorithms to Measure Area and Volume on 3D Face Models for Facial Surgeries

et al. 2023

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The increased availability and affordability of 3D scanning technology due to recent advancements have made pre and post-operative analysis for facial surgeries more convenient and accurate through the use of 3D patient scans. However, to perform a more comprehensive analysis using area and volume measurements, it is necessary to develop new software tools that include the appropriate algorithms. Without the right algorithms, any software tool cannot perform its intended tasks effectively. In this study, we introduce two algorithms along with their open-source code to calculate the surface area and volume of a specific subsection of a 3D model. The first algorithm partitions the area/volume into smaller segmentations and applies raycasting to find the points on the face surface, and then finds an approximate measurement depending on the resolution selected. In contrast, the second algorithm utilizes the vertex information stored in the 3D model file to calculate the area and volume measurements for the designated region. The advantage of the second algorithm is that it examines every point in the model, resulting in the highest degree of precision possible. We have also used ChatGPT to generate the code for area/volume calculation and found that the logic of the ChatGPT-generated code is surprisingly similar to the second algorithm. We have presented the results of the measurements on certain regions of the face for both of the algorithms and compared the temporal performance of the algorithms.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Algorithms to Measure Area and Volume on 3D Face Models for Facial Surgeries

et al. 2023

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“…Facial features (landmarks) and measurements that are in use by researchers and facial plastic surgeons were presented in a recent literature review article [21] and a free web-based facial analysis tool [22]. While there are over a hundred facial landmarks, we focused on the ones that are more relevant to facial plastic surgery.…”

Section: Methodsmentioning

confidence: 99%

Detecting Facial Landmarks on 3D Models Based on Geometric Properties—A Review of Algorithms, Enhancements, Additions and Open-Source Implementations

et al. 2023

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Facial landmark detection, a crucial aspect of face recognition, is widely used in various fields, such as facial surgeries, biometrics, and surveillance systems. With the advancement of affordable and capable 3D scanning technologies, research on automatically detecting facial landmarks on 3D models is gaining momentum. Utilizing the geometric properties of 3D facial models, researchers have developed algorithms for various landmarks with varying levels of accuracy. In this study, we reviewed existing literature and developed algorithms for thirty-eight landmarks using geometric properties and statistical information about facial measurements. The algorithms for thirty landmarks are original contributions to the literature. We provide the implementation of all the algorithms as open-source Python code, along with the pseudocode for both our algorithms and those found in the literature. To the best of our knowledge, this study covers the largest number of facial landmark detection algorithms based on the geometric properties of 3D models. This is the first study that provides the implementation of the algorithms along with detailed pseudocode. The results of the algorithms are presented by calculating the mean, median, standard deviation, minimum, and maximum of the errors and depicting the histogram for each landmark over a hundred 3D facial scans. The results show that geometric properties and statistics can be utilized to achieve more robust solutions for facial landmark detection.

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“…We made sure that the 3D models had measurement values within realistic limits. We used the Face Analyzer tool [36], [37] to measure the 3D models and then compare them with the statistics from the 3D Facial Norms Database [38] listed at Facebase.org [39]. We checked the 3D models against the following measurements: 1) Maximum Cranial Width, 2) Cranial Base Width, 3) Morphological Facial Height, 4) Lower Facial Height, 5) Nasal Width, 6) Nasal Height, and 7) Nasal Protrusion; to make sure each measurement of a 3D model is within one standard deviation of the mean value for the supposed age of the 3D model.…”

Section: A Creation Of 3d Modelsmentioning

confidence: 99%

Utilization of Machine Learning for the Objective Assessment of Rhinoplasty Outcomes

et al. 2023

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Machine Learning started to provide solutions to various challenges in many fields, including medicine. The objective assessment of rhinoplasty results has been a challenge since the assessment of beauty is subjective in nature. This study explores if Machine Learning can be used to accomplish the complex task of objective evaluating the outcome evaluation and automated scoring for rhinoplasty. We introduce a methodology to map the aesthetics of visual appearance to the quantified measurements of presurgery, planned outcome, and post-surgery using machine learning. To develop the methodology, we generated synthetic 3D models utilizing artificial intelligence tools and applied various nasal deformities to simulate the pre-surgery, planned outcome, and post-surgery scans of rhinoplasty patients. The simulated outcomes were scored by reviewing the 3D visuals and corresponding measurements to prepare the training data for machine learning models. AutoGluon AutoML framework is used to generate the best-performing machine learning model. We successfully developed machine learning models with accuracy between 82% to 88% depending on the scoring method. We also identified the measurements that are highly influential in determining the scores. This is the first study that correlates the visual appearance and quantitative facial measurements of simulated rhinoplasty outcomes. The results suggest that an AI-based objective rhinoplasty outcome scoring tool is possible when machine learning algorithms are trained using consensus scores along with patients' pre-surgery, planned, and post-surgery measurements. This study introduces a methodology regarding how to map the aesthetics of visual appearance to the quantified measurements of pre-surgery, planned outcome, and post-surgery using machine learning.

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

Cited by 12 publications

References 22 publications

Algorithms to Measure Area and Volume on 3D Face Models for Facial Surgeries

Algorithms to Measure Area and Volume on 3D Face Models for Facial Surgeries

Detecting Facial Landmarks on 3D Models Based on Geometric Properties—A Review of Algorithms, Enhancements, Additions and Open-Source Implementations

Utilization of Machine Learning for the Objective Assessment of Rhinoplasty Outcomes

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