Outcomes of external septorhinoplasty in a Turkish male population

Kilci, Gamze Didem; Başer, Eralp; Verim, Ayşegül; Çalım, Ömer Faruk; Veyseller, Bayram; Özturan, Orhan; Altıntaş, Ahmet; Çelik, Mustafa

doi:10.1016/j.bjorl.2017.04.010

Cited by 2 publications

(1 citation statement)

References 36 publications

(53 reference statements)

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“…Nevertheless, few studies to date have used photography to find the distinguishing features for different ethnic groups, and therefore is the focus of the present study. Most healthcare literature [2], [4], [8], [23], [24] uses anthropometrical measurements-based classification scheme, while most computer vision literature [10]- [12] uses appearance-based classification schemes instead. Although both healthcare and computer vision literature use different classification schemes, they focus on distinguishing features in one or two major ethnic groups, with almost no attention given to sub-ethnic groups, such as Chinese, Indian, and Malay in the Asian category.…”

Section: Introductionmentioning

confidence: 99%

Facial Anthropometric Measurements and Photographs — An Interdisciplinary Study

et al. 2020

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In recent years, automatic facial analysis has attracted much interest among computer science researchers in the healthcare and computer vision fields studying facial anthropometric measurements using photographs. However, to date, there have been no healthcare or computer vision publications that use standardized photographs to differentiate features between sub-ethnic groups by leveraging the power of machine learning on two-dimensional computer vision benchmark data sets (2D CVBDs). Thus, the present work is an interdisciplinary study at the interface of healthcare and computer vision fields that attempts to fill this literature gap where we explore the use of machine learning on 2,789 photographs from eleven 2D CVBDs to identify k top discriminative features in major and sub-ethnic groups. These features are ranked based on information gain values and p-values. We also provide a comprehensive analysis of using information-gain-based and p-value-based features. Our machine learning model achieves an accuracy of 96-99%, and our findings reveal that information-gain-based features have the upper hand over p-valuebased features. The top three information-gain-based features in sub-ethnic groups are: dn (distance from the tip of the nose to the center of the mouth), hf (face height) and wn (nose width), while the top three information-gain-based features in major ethnic groups are: de (distance between the inner corners of the eyelids), hf and dn. These results are then compared to the results obtained using standard deep learning techniques such as OxfordNet (VGG16), Residual Networks (ResNet50), and Inception-V3, where accuracy of 90-94% was seen. We hope that these findings will lead to future collaboration between computer vision and healthcare researchers studying facial anthropometric measurement studies.

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Section: Introductionmentioning

confidence: 99%

Facial Anthropometric Measurements and Photographs — An Interdisciplinary Study

et al. 2020

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Incidence of Postoperative Adverse Events after Rhinoplasty: A Systematic Review

Sharif‐Askary

Carlson

Noord

et al. 2020

Plastic &Amp; Reconstructive Surgery

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Background: Adverse events after rhinoplasty vary in etiology and severity, a fact that is reflected in the current American Society of Plastic Surgeons rhinoplasty consent form. However, there is currently no literature providing a comprehensive summation of evidence-based quantifiable risk of adverse events after rhinoplasty. Given this limitation, patients considering rhinoplasty are unable to fully ascertain preoperative risk, and the ability of physicians to obtain true informed consent is similarly flawed. This systematic review provides the first rigorous, comprehensive, and quantitative reporting of adverse events after rhinoplasty. Methods: This review was registered on the International Prospective Register of Systematic Reviews (PROSPERO) database (registration no. CRD42018081826) in April of 2018. Eligible articles were published in peer-reviewed journals with available abstracts and full-text articles. Interventions included primary functional, aesthetic, and combined functional/aesthetic rhinoplasty. The following data were extracted: study size, population characteristics, indication, surgical approach, concomitant procedures, and incidence of adverse events. Results: A search yielded 3215 publications for title and abstract screening. Three hundred twenty-two were eligible for full-text review. Thirty-six met final inclusion criteria. A total of 13 adverse events were reported among these studies and included need for revision (0 to 10.9 percent), infection (0 to 4 percent), dehiscence (0 to 5 percent), bleeding (0 to 4.1 percent), septal perforation (0 to 2.6 percent), nasal airway obstruction requiring revision (0 to 3 percent), and hypertrophic scarring (0 to 1.5 percent). Conclusions: This systematic review presents the first comprehensive and quantitative reporting of adverse event frequency after rhinoplasty. This is a crucial tool for preoperative patient counseling and an essential adjunct in the acquisition of informed consent. Future investigations will benefit from transparency and standardization of reporting to further quantify adverse event rates.

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Outcomes of external septorhinoplasty in a Turkish male population

Abstract: This study demonstrated that outcomes for nasofrontal angle, nasolabial angle and nasal tip projection ratios analyzed using the Rhinobase program in patients who underwent external septorhinoplasty were similar to reference values for the Turkish population.

Cited by 2 publications

References 36 publications

Facial Anthropometric Measurements and Photographs — An Interdisciplinary Study

Facial Anthropometric Measurements and Photographs — An Interdisciplinary Study

Incidence of Postoperative Adverse Events after Rhinoplasty: A Systematic Review

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