Background In the past decade there has been a dramatic increase in trends related to body-shaping procedures. According to the American Society of Plastic Surgeons, nearly 300,000 breast augmentation procedures were conducted in 2019. 1 Learning the ideal shape of a breast, and which esthetics lead to public perception of the most attractive breast is beneficial to properly performing these procedures. Objectives The authors aimed to quantify public perception of the attraction to breast shape by measuring public opinion for various esthetic elements of breast anatomy, and linking this to various demographic factors. Methods Survey responses were collected from 1,000 users of Amazon Mechanical Turk to collect demographic data as well as ask users to rank preferences for randomized image panels of breast proportions. Responses were collected and analyzed to find the overall ideal breast proportions. Results In total, 1000 responses were collected, with 960 being used for analysis. Overall, a majority of respondents were male (60%), with a plurality belonging to the age group of 25-34 years old (49.3%). The most notable preference between all groups was the breast projection proportion and the preferred nipple direction, with preferences of 1.0 and a frontal nipple direction, respectively. Breast width to shoulder width ratio also had a clear preference among the crowd, with 105% being the preferred percentage, and the 25-34 age group having a very strong preference for this. Conclusions The authors used a crowdsourcing survey technique with randomized image panels to analyze ideal breast preference using images of various anatomical traits of the female breast. It was concluded that crowdsourcing can be a favorable technique for learning ideal overall preferences for specific anatomy.
Background: Reliable and valid assessments of the visual endpoints of aesthetic surgery procedures are needed. Currently, most assessments are based on the opinion of patients and their plastic surgeons. The objective of this research was to analyze the reliability of crowdworkers assessing de-identified photographs using a validated scale that depicts lower facial aging. Methods: Twenty photographs of the facial nasolabial region of various non-identifiable faces were obtained for which various degrees of facial aging were present. Independent crowds of 100 crowd workers were tasked with assessing the degree of aging using a photograph numeric scale. Independent groups of crowdworkers were surveyed at 4 different times (weekday daytime, weekday nighttime, weekend daytime, weekend nighttime), once a week for 2 weeks. Results: Crowds assessing midface region photographs had an overall correlation of R = 0.979 (weekday daytime R = 0.991; weekday nighttime R = 0.985; weekend daytime R = 0.997; weekend nighttime R = 0.985). Bland−Altman test for test-retest agreement showed a normal distribution of assessments over the various times tested, with the differences in the majority of photographs being within 1 SD of the average difference in ratings. Conclusions: Crowd assessments of facial aging in de-identified photographs displayed very strong concordance with each other, regardless of time of day or week. This shows promise toward obtaining reliable assessments of pre and postoperative results for aesthetic surgery procedures. More work must be done to quantify the reliability of assessments for other pretreatment states or the corresponding results following treatment.
Background Breast augmentation procedures are one of the most commonly occurring aesthetic procedures in the United States. Some research has analyzed specific cultural preferences for breast characteristics. However, little work has focused on the general public’s overall perception of the ideal breast or validated them with patient photos. Objectives To validate crowdsourced perceptions of breast shapes with their alignment to the aesthetics of patients before and after a breast augmentation. Methods A prospective cross-sectional study was performed using random participants enrolled through the Amazon Mechanical Turk crowdsourcing platform (Amazon Web Services, Amazon, Seattle, WA) to obtain participant opinions of how closely patient breasts aligned with previously obtained results of four ideal breast characteristics. Outcomes were reported based on the correlation between breast attractiveness and alignment to ideal breast characteristics, both before and after breast implant procedures. Results A total of 2306 responses from 737 study participants reported patient photo alignment with the ideal breast projection proportion (1.0) as having the highest correlation to opinions of heightened aesthetic beauty (R=0.98, p < 0.001), and the ideal nipple direction (front) as having the lowest correlation to aesthetic beauty (R=0.90, p < 0.001). Notably, younger age groups (18-24) and participants with a high school diploma or less, rated patients as less attractive overall, while married and wealthy individuals reported higher attraction levels to the patient photos. Conclusions Crowdsourcing can be a useful tool for aesthetic surgery preferences and has helped reveal key takeaways. The importance of the four breast characteristics has been validated, with alignment to all four characteristics tested having a high level of correlation to aesthetic preferences. Additionally, certain differences in aesthetic preference across demographic groups are a topic to further investigate in future research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.