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Applications of Machine Learning in Facial Cosmetic Surgeries: A Scoping Review

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  • Facial Surgery
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Aesthetic Plastic Surgery Aims and scope Submit manuscript

Abstract

Objective

To review the application of machine learning (ML) in the facial cosmetic surgeries and procedures

Methods and materials

Electronic search was conducted in PubMed, Scopus, Embase, Web of Science, ArXiv and Cochrane databases for the studies published until August 2022. Studies that reported the application of ML in various fields of facial cosmetic surgeries were included. The studies’ risk of bias (ROB) was assessed using the QUADAS-2 tool and NIH tool for before and after studies.

Results

From 848 studies, a total of 29 studies were included and categorized in five groups based on the aim of the studies: outcome evaluation (n = 8), face recognition (n = 7), outcome prediction (n = 7), patient concern evaluation (n = 4) and diagnosis (n = 3). Total of 16 studies used public data sets. ROB assessment using QUADAS-2 tool revealed that six studies were at low ROB, five studies were at high ROB, and others had moderate ROB. All studies assessed with NIH tool showed fair quality. In general, all studies showed that using ML in the facial cosmetic surgeries is accurate enough to benefit both surgeons and patients.

Conclusion

Using ML in the field of facial cosmetic surgery is a novel method and needs further studies, especially in the fields of diagnosis and treatment planning. Due to the small number of articles and the qualitative analysis conducted, we cannot draw a general conclusion about the impact of ML in the sphere of facial cosmetic surgery.

Level of Evidence IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

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References

  1. Yu K-H, Beam AL, Kohane IS (2018) Artificial intelligence in healthcare. Nat Biomed Eng 2(10):719–731

    Article  PubMed  Google Scholar 

  2. Rong G et al (2020) Artificial intelligence in healthcare: review and prediction case studies. Engineering 6(3):291–301

    Article  Google Scholar 

  3. Jiang F et al (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vascular Neurol 2(4).

  4. Choi RY et al (2020) Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol 9(2):14–14

    PubMed  PubMed Central  Google Scholar 

  5. Siddiqui TA, Sukhia RH, Ghandhi D (2022) Artificial intelligence in dentistry, orthodontics and orthognathic surgery: a literature review. J Pak Med Assoc 72(Suppl 1)(2):S91–S96

    Google Scholar 

  6. Eldaly AS et al (2022) Simulation and artificial intelligence in rhinoplasty: a systematic review. Aesthetic Plastic Surg 46(5):2368–2377

    Article  Google Scholar 

  7. Ryu JY, Chung HY, Choi KY (2021) Potential role of artificial intelligence in craniofacial surgery. Arch Craniofacial Surg 22(5):223–231

    Article  Google Scholar 

  8. Hwang J-J et al (2019) An overview of deep learning in the field of dentistry. Imaging Sci Dentist 49(1):1–7

    Article  Google Scholar 

  9. Liang X et al (2021) Artificial intelligence in plastic surgery: applications and challenges. Aesthetic Plastic Surg 45(2):784–790

    Article  Google Scholar 

  10. Chandawarkar A et al (2020) A practical approach to artificial intelligence in plastic surgery. Aesthetic Surgery Journal Open Forum 2(1):ojaa001

  11. Gibstein AR et al (2021) Facelift surgery turns back the clock: artificial intelligence and patient satisfaction quantitate value of procedure type and specific techniques. Aesthetic Surg J 41(9):987–999

    Article  Google Scholar 

  12. Dagli MM et al (2021) The use of artificial intelligence and machine learning in surgery: a comprehensive literature review. Am Surg 27:31348211065101.

  13. Mantelakis A (2021) The clinical applications of machine learning in plastic and reconstructive surgery: a systematic review. Br J Surg 108(Suppl 2):znab134.006

  14. Khedgaonkar R, Singh K, Raghuwanshi M (2021) Local plastic surgery-based face recognition using convolutional neural networks. In: N P, Kautish S, Peng SL (eds.) Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics. Elsevier, Amsterdam, pp 215–246

  15. Mantelakis A et al (2021) Machine learning demonstrates high accuracy for disease diagnosis and prognosis in plastic surgery. Plast Reconstr Surg Glob Open 9(6):e3638

    Article  PubMed  PubMed Central  Google Scholar 

  16. Eldaly AS et al (2022) Simulation and artificial intelligence in rhinoplasty: a systematic review. Aesthetic Plastic Surg 46(5):2368–2377

    Article  Google Scholar 

  17. Moher D et al (2015) Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev 4(1):1–9

    Article  PubMed  PubMed Central  Google Scholar 

  18. Whiting PF et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529–36

    Article  PubMed  Google Scholar 

  19. Sable AH (2021) Customized adaptive gradient and orientation histogram for faces altered by face surgery. In: 2021 7th International Conference on Electrical Energy Systems (ICEES), pp. 593–9

  20. Ali Shah SA, Bennamoun M, Molton M (2018) A fully automatic framework for prediction of 3D facial rejuvenation. In: 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ), pp. 1–6

  21. Yoelin S et al (2022) The use of a novel artificial intelligence platform for the evaluation of rhytids. Aesthetic Surg J 42(11):NP688–NP694

  22. Alrabiah A, Alduailij M, Crane M (2019) Computer-based approach to detect wrinkles and suggest facial fillers. Int J Adv Comp Sci Appl 10(9)

  23. Kim M, Kang S, Lee BD (2022) Evaluation of automated measurement of hair density using deep neural networks. Sensors 22(2):650. https://doi.org/10.3390/s22020650.

  24. Bahçeci Şimşek İ, Şirolu C (2021) Analysis of surgical outcome after upper eyelid surgery by computer vision algorithm using face and facial landmark detection. Graefes Arch Clin Exp Ophthalmol 259(10):3119–3125.

  25. Yoo TK, Choi JY, Kim HK (2020) A generative adversarial network approach to predicting postoperative appearance after orbital decompression surgery for thyroid eye disease. Comput Biol Med 118:103628

    Article  PubMed  Google Scholar 

  26. Shah SAA, Bennamoun M, Molton MK (2019) Machine learning approaches for prediction of facial rejuvenation using real and synthetic data. IEEE Access 7:23779–23787

  27. Tuan HNA, Hai NDX, Thinh NT (2022) Shape prediction of nasal bones by digital 2D-photogrammetry of the nose based on convolution and back-propagation neural network. Comput Math Methods Med 2022:5938493

    Article  PubMed  PubMed Central  Google Scholar 

  28. Lamassoure L et al (2021) Anatomical subject validation of an instrumented hammer using machine learning for the classification of osteotomy fracture in rhinoplasty. Med Eng Phy 95:111–116

    Article  PubMed  Google Scholar 

  29. Chinski H et al (2022) An artificial intelligence tool for image simulation in rhinoplasty. Facial Plast Surg 38(2):201–206

    Article  PubMed  Google Scholar 

  30. Khetpal S et al (2022) Perceived age and attractiveness using facial recognition software in rhinoplasty patients: a proof-of-concept study. J Craniofac Surg 33(5):1540–1544

    Article  PubMed  Google Scholar 

  31. Dorfman R et al (2020) Making the subjective objective: machine learning and rhinoplasty. Aesthet Surg J 40(5):493–498

    Article  PubMed  Google Scholar 

  32. Štěpánek L, Kasal P, Měšťák J (2019) Machine-learning at the service of plastic surgery: a case study evaluating facial attractiveness and emotions using R language. In: Proceedings of the Federated Conference on Computer Science and Information Systems, pp 107–112.

  33. Štěpánek L, Kasal P, Mestak J (2018) Evaluation of facial attractiveness for purposes of plastic surgery using machine-learning methods and image analysis. In: 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 1–6

  34. Gibstein AR et al (2021) Facelift surgery turns back the clock: artificial intelligence and patient satisfaction quantitate value of procedure type and specific techniques. Aesthet Surg J 41(9):987–999

    Article  PubMed  Google Scholar 

  35. Boonipat T, Lin J, Bite U (2021) Detection of baseline emotion in brow lift patients using artificial intelligence. Aesthetic Plast Surg 45(6):2742–2748

    Article  PubMed  Google Scholar 

  36. Hallac RR et al (2021) Assessing outcomes of ear molding therapy by health care providers and convolutional neural network. Sci Rep 11(1):17875

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Thomas PBM et al (2020) An artificial intelligence approach to the assessment of abnormal lid position. Plast Reconstr Surg Glob Open 8(10):e3089

    Article  PubMed  PubMed Central  Google Scholar 

  38. Chen K et al (2020) Facial Recognition Neural Networks Confirm Success of Facial Feminization Surgery. Plast Reconstr Surg 145(1):203–209

    Article  CAS  PubMed  Google Scholar 

  39. Boczar D et al (2020) Artificial intelligent virtual assistant for plastic surgery patient's frequently asked questions: a pilot study. Ann Plast Surg 84(4):e16–e21

    Article  CAS  PubMed  Google Scholar 

  40. Patel R et al (2022) Applying machine learning to determine popular patient questions about mentoplasty on social media. Aesthetic Plastic Surg 46(5):2273–2279

  41. Tseng CC et al (2021) Characterizing patient questions before and after rhinoplasty on social media: a big data approach. Aesthetic Plast Surg 45(4):1685–1692

    Article  PubMed  Google Scholar 

  42. Levites HA et al (2019) The use of emotional artificial intelligence in plastic surgery. Plast Reconstr Surg 144(2):499–504

    Article  CAS  PubMed  Google Scholar 

  43. Chanchua A, Chentanez N (2021) DeltaFace: fully automatic 3D facial cosmetic surgery simulation. In: 2021 25th International Computer Science and Engineering Conference (ICSEC), pp 246–251

  44. Sabharwal T, Gupta R (2022) A deep learning approach to recognize faces after plastic surgery. In: Bansal RC, Agarwal A, Jadoun VK (eds.) Advances in Energy Technology. Lecture Notes in Electrical Engineering, vol. 766. Springer, Singapore.

  45. Sabharwal T, Gupta R (2022) Deep facial recognition after medical alterations. Multimedia Tools Appl 81(18):25675–25706

    Article  Google Scholar 

  46. Bansal A, Shetty N (2018) Matching before and after surgery faces. Proc Comp Sci 132:141–148

    Article  Google Scholar 

  47. Borsting E et al (2020) Applied deep learning in plastic surgery: classifying rhinoplasty with a mobile app. J Craniofac Surg 31(1):102–106

    Article  PubMed  Google Scholar 

  48. Suri S et al (2018) On matching faces with alterations due to plastic surgery and disguise. In: 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp 1–7

  49. Banan M, Eftekhari Moghadam AM, Broumandnia A (2013) Modified after surgical face recognition using RBF neural networks and local Gabor binary patterns. In: 2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp. 1–4

  50. Bakhshali MA, Shamsi M, Golzarfar A (2012) Application of SOM network to study facial skin segmentation after facial surgery. In: 2012 IEEE Symposium on Industrial Electronics and Applications pp. 310–314

  51. Ryu JY, Chung HY, Choi KY (2021) Potential role of artificial intelligence in craniofacial surgery. Arch Craniofac Surg 22(5):223–231

    Article  PubMed  PubMed Central  Google Scholar 

  52. Jiang F et al (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2(4):230–243

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This research did not receive any specific grant from the public, commercial, or not-for-profit funding agencies.

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Authors and Affiliations

  1. Student research committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, 1983963113, Iran

    Nima Ahmadi, Maral Niazmand, Ali Ghasemi & Sadra Mohaghegh

  2. Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Nima Ahmadi, Maral Niazmand, Ali Ghasemi, Sadra Mohaghegh & Saeed Reza Motamedian

  3. Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, 1983963113, Iran

    Saeed Reza Motamedian

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  1. Nima Ahmadi
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Ahmadi, N., Niazmand, M., Ghasemi, A. et al. Applications of Machine Learning in Facial Cosmetic Surgeries: A Scoping Review. Aesth Plast Surg 47, 1377–1393 (2023). https://doi.org/10.1007/s00266-023-03379-y

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  • DOI: https://doi.org/10.1007/s00266-023-03379-y

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