Evaluation of Changes in Facial Attractiveness and Estimated Facial Age After Blepharoplasty with an Artificial Intelligence Algorithm


Abstract views: 277 / PDF downloads: 56

Authors

DOI:

https://doi.org/10.58600/eurjther1648

Keywords:

Blepharoplasty, Artificial intelligence, Facial age, Facial attractiveness

Abstract

Objective: The aim of this study is to evaluate the effects of blepharoplasty operation on facial attractiveness and estimated facial age with an artificial intelligence-based algorithm over pre- and post-treatment facial photographs. In addition, it is aimed to make a comparison by reviewing the observable changes according to gender and operation type (upper, lower, combined).

Methods: Preoperative and postoperative photos of patients who underwent open access and copyright-free blepharoplasty operation on social media platforms (instagram and youtube) were collected. The photos were evaluated by an artificial intelligence algorithm trained to estimate facial age and evaluate facial attractiveness.

Results: A total of 541 patients, of which 454 (83.92%) were female and 87 (16.08%) were male. When all patients were evaluated without subgrouping, there was a -1.91±3.35 years younger face age and 0.43±0.64 point increase in facial attractiveness (p<0.005).

Conclusion: In this study, the effects of blepharoplasty on facial attractiveness and apparent age are presented with quantitative data. In addition, it has been concluded that artificial intelligence can be used in scoring the apparent age and facial attractiveness after blepharoplasty.

Metrics

Metrics Loading ...

References

Zoumalan CI, Roostaeian J (2016) Simplifying Blepharoplasty. Plast Reconstr Surg 137(1):196e-213e. https://doi.org/10.1097/PRS.0000000000001906

Finsterer J (2003) Ptosis: causes, presentation, and management. Aesthetic Plast Surg 27(3):193–204. https://doi.org/10.1007/s00266-003-0127-5

Drolet BC, Sullivan PK (2014) Evidence-based medicine: Blepharoplasty. Plast Reconstr Surg 133(5):1195–1205. https://doi.org/10.1097/PRS.0000000000000087

Bater KL, Ishii M, Nellis JC, et al (2018) A Dual Approach to Understanding Facial Perception Before and After Blepharoplasty. JAMA Facial Plast Surg 20(1):43–49. https://doi.org/10.1001/jamafacial.2017.1099

Bahsi I, Orhan M, Kervancioglu P, et al (2021) Craniofacial Anthropometry of Healthy Turkish Young Adults: Outer Canthal, Inner Canthal, Palpebral Fissure, and Interpupillary Distances. J Craniofac Surg 32(5). https://doi.org/10.1097/SCS.0000000000007240

Evereklioglu C, Doganay S, ER H, Tercan M, Gunduz A, Balat A, et al. (2001) Interpupillary index: a new parameter for hypo-hypertelorism. J Cranio-Maxillofacial Surg 29(4):191–4. https://doi.org/10.1054/jcms.2001.0230

Schaal LF, de Souza Meneghim RL, Padovani CR, Schellini SA (2022) Upper eyelid blepharoplasty and associated ancillary procedures to improve cosmesis. J Fr Ophtalmol 45(1):53–56. https://doi.org/10.1016/j.jfo.2021.08.007

Zhang Y, Xiao Z (2022) Upper Eyelid Blepharoplasty Improved the Overall Periorbital Aesthetics Ratio by Enhancing Harmony Between the Eyes and Eyebrows. Clin Cosmet Investig Dermatol 15:1969–1978. https://doi.org/10.2147/CCID.S385057

Patcas R, Bernini DAJ, Volokitin A, et al (2019) Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. Int J Oral Maxillofac Surg 48(1):77–83. https://doi.org/10.1016/j.ijom.2018.07.010

Prahl-Andersen B, Boersma H, Van der Linden FP, Moore AW (1979) Perceptions of dentofacial morphology by laypersons, general dentists, and orthodontists. J Am Dent Assoc 98(2):209–212

Stirling J, Latchford G, Morris DO, et al (2007) Elective orthognathic treatment decision making: a survey of patient reasons and experiences. J Orthod 34(2):113–127

Yin L, Jiang M, Chen W, et al (2014) Differences in facial profile and dental esthetic perceptions between young adults and orthodontists. Am J Orthod Dentofac Orthop 145(6):750–756

Shmotkin D (1990) Subjective Well-Being as a Function of Age and Gender: A Multivariate Look for Differentiated Trends. Soc Indic Res 23(3):201–230

Patcas R, Timofte R, Volokitin A, et al (2019) Facial attractiveness of cleft patients: a direct comparison between artificial-intelligence-based scoring and conventional rater groups. Eur J Orthod 41(4):428–433. https://doi.org/10.1093/ejo/cjz007

Obwegeser D, Timofte R, Mayer C, et al (2022) Using artificial intelligence to determine the influence of dental aesthetics on facial attractiveness in comparison to other facial modifications. Eur J Orthod 44(4):445–451. https://doi.org/10.1093/ejo/cjac016

Balel Y, Şen E, Akbulut N, et al (2023) Evaluation of the effect of changes in cephalometric values after orthognathic surgery on estimated age and facial aesthetics. J Stomatol Oral Maxillofac Surg 101461. https://doi.org/10.1016/j.jormas.2023.101461

Zhang Z, Song Y, Qi H (2017) Age progression/regression by conditional adversarial autoencoder. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 5810–5818

Rothe R, Timofte R, Van Gool L (2018) Deep expectation of real and apparent age from a single image without facial landmarks. Int J Comput Vis 126(2):144–157

Ma DS, Correll J, Wittenbrink B (2015) The Chicago face database: A free stimulus set of faces and norming data. Behav Res Methods 47(4):1122–1135

Rothe R, Timofte R, Van Gool L (2016) Some like it hot-visual guidance for preference prediction. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 5553–5561

Tabachnick BG, Fidell LS (2013) Using multivariate statistics, 6th edn Boston. Ma Pearson

Kim YS, Kim BS, Kim HS, et al (2021) Factors Influencing Patient Satisfaction with Upper Blepharoplasty in Elderly Patients. Plast Reconstr surgery Glob open 9:e3727. https://doi.org/10.1097/GOX.0000000000003727

Sai P-K, Wang J-G, Teoh E-K (2015) Facial age range estimation with extreme learning machines. Neurocomputing 149:364–372

Chao W-L, Liu J-Z, Ding J-J (2012) Facial age estimation based on label-sensitive learning and age-specific local regression. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 1941–1944

Hu Z, Wen Y, Wang J, et al (2017) Facial Age Estimation With Age Difference. IEEE Trans image Process a Publ IEEE Signal Process Soc 26(7):3087–3097. https://doi.org/10.1109/TIP.2016.2633868

Guo G, Mu G, Fu Y, et al (2009) A study on automatic age estimation using a large database. In: 2009 IEEE 12th International Conference on Computer Vision. IEEE, pp 1986–1991

Kiekens RMA, Kuijpers-Jagtman AM, van‘t Hof MA, et al (2008) Facial esthetics in adolescents and its relationship to “ideal” ratios and angles. Am J Orthod Dentofac Orthop 133(2):188-e1

Knight H, Keith O (2005) Ranking facial attractiveness. Eur J Orthod 27:340–348

Figure 1

Downloads

Published

2023-06-19

How to Cite

Balel, Y. (2023). Evaluation of Changes in Facial Attractiveness and Estimated Facial Age After Blepharoplasty with an Artificial Intelligence Algorithm. European Journal of Therapeutics, 29(4), 883–890. https://doi.org/10.58600/eurjther1648

Issue

Section

Original Articles

Categories