Rhinoplasty Technique for Improving Nasal Base Aesthetics

Cabbarzade, Cavid

doi:10.1097/sap.0000000000003505

Cited by 2 publications

(6 citation statements)

References 18 publications

(83 reference statements)

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“…The limitations of prior studies include the use of statistical analysis to examine medical interventions in surgery [ 8 , 34 , 38 ], reliance on user participation until the result is obtained [ 4 , 32 , 37 ], and the inability of the presented methods to provide a quantitative measure of symmetry [ 4 , 32 , 37 , 39 ]. These studies have advantages in addressing the issues of a specific range of patients and offering computer-based approaches.…”

Section: Resultsmentioning

confidence: 99%

“…Some of the complications in rhinoplasty that significantly affect facial attractiveness [ 2 ] are asymmetry and deformation appearance [ 7 ]. Symmetry can be considered a major factor in nasal base esthetics [ 8 ] especially, by using selfies in the digital age, the rate of requests for a more symmetrical nose is increased [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…The important key is the lack of an accurate and rapid automated method to detect the landmarks of the nasal base. In other related research, the symmetry of the nasal base has been studied using some quantitative methods to survey the geometry features that are limited to the statistical methods [ 4 , 8 , 32 , 34 , 37 – 39 ]. Barnes and et.…”

Section: Introductionmentioning

confidence: 99%

“…Al [ 4 ] utilized the lateral deviation (symmetry) of the nasal base as a parameter of a polar function without calculating the value of the symmetry. [ 8 ] Presented a clinical technique to improve the symmetry of the columella and nostrils. Then used the test to compare the results of pre and post-operative based on patients’ opinion [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

“…In other related research, the symmetry of the nasal base has been studied using some quantitative methods to survey the geometry features that are limited to the statistical methods [ 4 , 8 , 32 , 34 , 37 – 39 ]. Barnes and et.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Ashoori,

Zoroofi,

Sadeghi

2024

J Digit Imaging. Inform. med.

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Ashoori,

Zoroofi,

Sadeghi

2024

J Digit Imaging. Inform. med.

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Correcting Different Degrees of Droopy Nose, an Overview of Techniques and Strategies at Tertiary Facial Plastic Center. A Retrospective Analysis

Alenazi,

Alrabiah,

Bafaqeeh

2024

Journal of Craniofacial Surgery

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Background: The variety of noses in the mixed Saudi population keeps rhinoplasty surgeons on their toes. The main treatment goal for drooping nose tips is the first rotation of the nasal tip superiorly. Although droopy nose is a common disease in Saudi Arabia, none of the previous studies recognized the general features of droopy nose. Methods: A retrospective analysis of 352 patients with nasal drooping nose from 2016 to 2022. The main outcome measurements were general characteristics of nasal tip ptosis, and the most common surgical techniques used to treat nasal tip ptosis in a tertiary hospital. Results: Analysis of 352 patients with droopy nose showed that 29.0% were between 25 and 29 years old, and 56.3% were female patients. The most common characteristic seen with droopy nasal tip was a significant dorsal hump in 64.8% of patients, followed by amorphous, boxy, and bulbous nasal tip in 33.5%. Significant dorsal hump and bifid tip were more common between 18 and 24. Meanwhile, amorphous, boxy, and bulbous nasal tip were more common in female individuals. The surgical techniques mostly addressed the underlying cause, which was tip grafting (83.0%), lateral osteotomy (77.3%), hump removal (66.5%), lateral crura method (61.9%), and septal extension graft (40.9%). Conclusions: The research concluded that the surgeon should be familiar with the patient’s underlying cause of nasal tip ptosis and use surgical techniques accordingly. To get a pleased long-term outcome, it is often essential to repair the inferiorly rotated tip using more than one surgical approach.

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Rhinoplasty Technique for Improving Nasal Base Aesthetics

Cited by 2 publications

References 18 publications

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

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

Correcting Different Degrees of Droopy Nose, an Overview of Techniques and Strategies at Tertiary Facial Plastic Center. A Retrospective Analysis

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