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J Cosmet Med 2023; 7(2): 60-65

Published online December 31, 2023

https://doi.org/10.25056/JCM.2023.7.2.60

Artificial-intelligence-automated machine learning as a tool for evaluating facial rhytid images

Alejandro Espaillat , MD

South Florida Eye Institute, Fort Lauderdale, FL, USA

Correspondence to :
Alejandro Espaillat
E-mail: drespaillat@evnmiami.com

Received: October 12, 2023; Revised: November 30, 2023; Accepted: December 6, 2023

© Korean Society of Korean Cosmetic Surgery & Medicine

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: The growing demand for nonsurgical cosmetic treatments necessitates a reliable diagnostic tool to assess the extent of aging, severity of facial wrinkles, and effectiveness of minimally invasive aesthetic procedures. This is crucial to accurately predict the need for botulinum neurotoxin type A neuromodulator injections during facial aesthetic rejuvenation.
Objective: This study aimed to determine the accuracy of artificial intelligence-based machine learning algorithms in analyzing facial rhytid images during facial aesthetic evaluation.
Methods: A prospective validation model was implemented using a dataset of 3,000 de-identified facial rhytid images from 600 patients in a community private medical spa aesthetic screening program. A neural architecture based on Google Cloud’s artificial intelligence-automated machine learning was developed to detect dynamic hyperkinetic skin lines in various facial muscles. Images were captured using a handheld iPad camera and labeled by an American board-certified ophthalmologist using established quantitative grading scales. The dataset was divided into training (80%), validation (10%), and testing (10%) sets. The model’s performance was evaluated using the following metrics: area under the precision–recall curve, sensitivity, specificity, precision, and accuracy.
Results: Facial rhytid images were detected in 79.9%, 10.7%, and 9.3% of the training sets, respectively. The model achieved an area under the precision–recall curve of 0.943, with an accuracy of 91.667% and a recall of 81.881% at a threshold score of 0.5.
Conclusion: This study demonstrates the successful application of artificial-intelligence-automated machine learning in identifying facial rhytid images captured using simple photographic devices in a community-based private medical spa program. Thus, the potential value of machine-learning algorithms for evaluating the need for minimally invasive injectable procedures for facial aesthetic rejuvenation was established.

Keywords: artificial intelligence, BoNT/A neuromodulator injections, facial aesthetic rejuvenation, facial rhytid images, machine-learning algorithms

Fig. 1.Dynamic wrinkle images graded from 3 (moderate) to 4 (severe). (A) Corrugator lines; (B) orbicularis oris lines; (C) mentalis lines; (D) orbicularis oculi lines; (E) nasalis lines; (F) frontalis lines; (G) procerus lines.
  1. American Society of Plastic Surgeons (ASPS). Inaugural ASPS insights and trends report: cosmetic surgery 2022. Arlington Heights (IL): ASPS; 2022.
  2. American Society of Plastic Surgeons (ASPS). Plastic surgery statistics report: ASPS national clearinghouse of plastic surgery procedural statistics. Arlington Heights (IL): ASPS; 2020.
  3. Fisher GJ, Kang S, Varani J, Bata-Csorgo Z, Wan Y, Datta S, et al. Mechanisms of photoaging and chronological skin aging. Arch Dermatol 2002;138:1462-70.
    Pubmed CrossRef
  4. Small R. Botulinum toxin injection for facial wrinkles. Am Fam Physician 2014;90:168-75.
  5. Carruthers A, Carruthers J. A validated facial grading scale: the future of facial ageing measurement tools? J Cosmet Laser Ther 2010;12:235-41.
    Pubmed CrossRef
  6. Honeck P, Weiss C, Sterry W, Rzany B; Gladys Study Group. Reproducibility of a four-point clinical severity score for glabellar frown lines. Br J Dermatol 2003;149:306-10.
    Pubmed CrossRef
  7. Choi HI, Jung SK, Baek SH, Lim WH, Ahn SJ, Yang IH, et al. Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery. J Craniofac Surg 2019;30:1986-9. Erratum in: J Craniofac Surg 2020;31:1156.
    Pubmed CrossRef
  8. Carruthers A, Carruthers J, Hardas B, Kaur M, Goertelmeyer R, Jones D, et al. A validated brow positioning grading scale. Dermatol Surg 2008;34 Suppl 2:S150-4.
    Pubmed CrossRef
  9. Carruthers A, Carruthers J, Hardas B, Kaur M, Goertelmeyer R, Jones D, et al. A validated grading scale for forehead lines. Dermatol Surg 2008;34 Suppl 2:S155-60.
    CrossRef
  10. Carruthers A, Carruthers J, Hardas B, Kaur M, Goertelmeyer R, Jones D, et al. A validated grading scale for marionette lines. Dermatol Surg 2008;34 Suppl 2:S167-72.
    Pubmed CrossRef
  11. Carruthers A, Carruthers J, Hardas B, Kaur M, Goertelmeyer R, Jones D, et al. A validated grading scale for crow’s feet. Dermatol Surg 2008;34 Suppl 2:S173-8.
    CrossRef
  12. Balki I, Amirabadi A, Levman J, Martel AL, Emersic Z, Meden B, et al. Sample-size determination methodologies for machine learning in medical imaging research: a systematic review. Can Assoc Radiol J 2019;70:344-53.
    Pubmed CrossRef
  13. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-44.
    Pubmed CrossRef
  14. Faes L, Wagner SK, Fu DJ, Liu X, Korot E, Ledsam JR, et al. Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. Lancet Digit Health 2019;1:e232-42.
    Pubmed CrossRef
  15. Jacoba CMP, Doan D, Salongcay RP, Aquino LAC, Silva JPY, Salva CMG, et al. Performance of automated machine learning for diabetic retinopathy image classification from multi-field handheld retinal images. Ophthalmol Retina 2023;7:703-12.
    Pubmed CrossRef
  16. O’Byrne C, Abbas A, Korot E, Keane PA. Automated deep learning in ophthalmology: AI that can build AI. Curr Opin Ophthalmol 2021;32:406-12.
    Pubmed CrossRef
  17. Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 2018;29:1836-42.
    Pubmed CrossRef
  18. Yeong EK, Hsiao TC, Chiang HK, Lin CW. Prediction of burn healing time using artificial neural networks and reflectance spectrometer. Burns 2005;31:415-20.
    Pubmed CrossRef
  19. Borsting E, DeSimone R, Ascha M, Ascha M. Applied deep learning in plastic surgery: classifying rhinoplasty with a mobile app. J Craniofac Surg 2020;31:102-6.
    Pubmed CrossRef

Article

Original Article

J Cosmet Med 2023; 7(2): 60-65

Published online December 31, 2023 https://doi.org/10.25056/JCM.2023.7.2.60

Copyright © Korean Society of Korean Cosmetic Surgery & Medicine.

Artificial-intelligence-automated machine learning as a tool for evaluating facial rhytid images

Alejandro Espaillat , MD

South Florida Eye Institute, Fort Lauderdale, FL, USA

Correspondence to:Alejandro Espaillat
E-mail: drespaillat@evnmiami.com

Received: October 12, 2023; Revised: November 30, 2023; Accepted: December 6, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background: The growing demand for nonsurgical cosmetic treatments necessitates a reliable diagnostic tool to assess the extent of aging, severity of facial wrinkles, and effectiveness of minimally invasive aesthetic procedures. This is crucial to accurately predict the need for botulinum neurotoxin type A neuromodulator injections during facial aesthetic rejuvenation.
Objective: This study aimed to determine the accuracy of artificial intelligence-based machine learning algorithms in analyzing facial rhytid images during facial aesthetic evaluation.
Methods: A prospective validation model was implemented using a dataset of 3,000 de-identified facial rhytid images from 600 patients in a community private medical spa aesthetic screening program. A neural architecture based on Google Cloud’s artificial intelligence-automated machine learning was developed to detect dynamic hyperkinetic skin lines in various facial muscles. Images were captured using a handheld iPad camera and labeled by an American board-certified ophthalmologist using established quantitative grading scales. The dataset was divided into training (80%), validation (10%), and testing (10%) sets. The model’s performance was evaluated using the following metrics: area under the precision–recall curve, sensitivity, specificity, precision, and accuracy.
Results: Facial rhytid images were detected in 79.9%, 10.7%, and 9.3% of the training sets, respectively. The model achieved an area under the precision–recall curve of 0.943, with an accuracy of 91.667% and a recall of 81.881% at a threshold score of 0.5.
Conclusion: This study demonstrates the successful application of artificial-intelligence-automated machine learning in identifying facial rhytid images captured using simple photographic devices in a community-based private medical spa program. Thus, the potential value of machine-learning algorithms for evaluating the need for minimally invasive injectable procedures for facial aesthetic rejuvenation was established.

Keywords: artificial intelligence, BoNT/A neuromodulator injections, facial aesthetic rejuvenation, facial rhytid images, machine-learning algorithms

Fig 1.

Figure 1.Dynamic wrinkle images graded from 3 (moderate) to 4 (severe). (A) Corrugator lines; (B) orbicularis oris lines; (C) mentalis lines; (D) orbicularis oculi lines; (E) nasalis lines; (F) frontalis lines; (G) procerus lines.
Journal of Cosmetic Medicine 2023; 7: 60-65https://doi.org/10.25056/JCM.2023.7.2.60

References

  1. American Society of Plastic Surgeons (ASPS). Inaugural ASPS insights and trends report: cosmetic surgery 2022. Arlington Heights (IL): ASPS; 2022.
  2. American Society of Plastic Surgeons (ASPS). Plastic surgery statistics report: ASPS national clearinghouse of plastic surgery procedural statistics. Arlington Heights (IL): ASPS; 2020.
  3. Fisher GJ, Kang S, Varani J, Bata-Csorgo Z, Wan Y, Datta S, et al. Mechanisms of photoaging and chronological skin aging. Arch Dermatol 2002;138:1462-70.
    Pubmed CrossRef
  4. Small R. Botulinum toxin injection for facial wrinkles. Am Fam Physician 2014;90:168-75.
  5. Carruthers A, Carruthers J. A validated facial grading scale: the future of facial ageing measurement tools? J Cosmet Laser Ther 2010;12:235-41.
    Pubmed CrossRef
  6. Honeck P, Weiss C, Sterry W, Rzany B; Gladys Study Group. Reproducibility of a four-point clinical severity score for glabellar frown lines. Br J Dermatol 2003;149:306-10.
    Pubmed CrossRef
  7. Choi HI, Jung SK, Baek SH, Lim WH, Ahn SJ, Yang IH, et al. Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery. J Craniofac Surg 2019;30:1986-9. Erratum in: J Craniofac Surg 2020;31:1156.
    Pubmed CrossRef
  8. Carruthers A, Carruthers J, Hardas B, Kaur M, Goertelmeyer R, Jones D, et al. A validated brow positioning grading scale. Dermatol Surg 2008;34 Suppl 2:S150-4.
    Pubmed CrossRef
  9. Carruthers A, Carruthers J, Hardas B, Kaur M, Goertelmeyer R, Jones D, et al. A validated grading scale for forehead lines. Dermatol Surg 2008;34 Suppl 2:S155-60.
    CrossRef
  10. Carruthers A, Carruthers J, Hardas B, Kaur M, Goertelmeyer R, Jones D, et al. A validated grading scale for marionette lines. Dermatol Surg 2008;34 Suppl 2:S167-72.
    Pubmed CrossRef
  11. Carruthers A, Carruthers J, Hardas B, Kaur M, Goertelmeyer R, Jones D, et al. A validated grading scale for crow’s feet. Dermatol Surg 2008;34 Suppl 2:S173-8.
    CrossRef
  12. Balki I, Amirabadi A, Levman J, Martel AL, Emersic Z, Meden B, et al. Sample-size determination methodologies for machine learning in medical imaging research: a systematic review. Can Assoc Radiol J 2019;70:344-53.
    Pubmed CrossRef
  13. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-44.
    Pubmed CrossRef
  14. Faes L, Wagner SK, Fu DJ, Liu X, Korot E, Ledsam JR, et al. Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. Lancet Digit Health 2019;1:e232-42.
    Pubmed CrossRef
  15. Jacoba CMP, Doan D, Salongcay RP, Aquino LAC, Silva JPY, Salva CMG, et al. Performance of automated machine learning for diabetic retinopathy image classification from multi-field handheld retinal images. Ophthalmol Retina 2023;7:703-12.
    Pubmed CrossRef
  16. O’Byrne C, Abbas A, Korot E, Keane PA. Automated deep learning in ophthalmology: AI that can build AI. Curr Opin Ophthalmol 2021;32:406-12.
    Pubmed CrossRef
  17. Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 2018;29:1836-42.
    Pubmed CrossRef
  18. Yeong EK, Hsiao TC, Chiang HK, Lin CW. Prediction of burn healing time using artificial neural networks and reflectance spectrometer. Burns 2005;31:415-20.
    Pubmed CrossRef
  19. Borsting E, DeSimone R, Ascha M, Ascha M. Applied deep learning in plastic surgery: classifying rhinoplasty with a mobile app. J Craniofac Surg 2020;31:102-6.
    Pubmed CrossRef

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