IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2022i1p468-d1017146.html
   My bibliography  Save this article

A Systematic Analysis of 3D Deformation of Aging Breasts Based on Artificial Neural Networks

Author

Listed:
  • Jun Zhang

    (School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China)

  • Ruixin Liang

    (Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, New Territories, Hong Kong, China)

  • Newman Lau

    (School of Design, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China)

  • Qiwen Lei

    (School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China)

  • Joanne Yip

    (School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
    Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, New Territories, Hong Kong, China)

Abstract

The measurement and prediction of breast skin deformation are key research directions in health-related research areas, such as cosmetic and reconstructive surgery and sports biomechanics. However, few studies have provided a systematic analysis on the deformations of aging breasts. Thus, this study has developed a model order reduction approach to predict the real-time strain of the breast skin of seniors during movement. Twenty-two women who are on average 62 years old participated in motion capture experiments, in which eight body variables were first extracted by using the gray relational method. Then, backpropagation artificial neural networks were built to predict the strain of the breast skin. After optimization, the R-value for the neural network model reached 0.99, which is within acceptable accuracy. The computer-aided system of this study is validated as a robust simulation approach for conducting biomechanical analyses and predicting breast deformation.

Suggested Citation

  • Jun Zhang & Ruixin Liang & Newman Lau & Qiwen Lei & Joanne Yip, 2022. "A Systematic Analysis of 3D Deformation of Aging Breasts Based on Artificial Neural Networks," IJERPH, MDPI, vol. 20(1), pages 1-18, December.
    • Handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:468-:d:1017146
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/1/468/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/1/468/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Howdon, Daniel & Rice, Nigel, 2018. "Health care expenditures, age, proximity to death and morbidity: Implications for an ageing population," Journal of Health Economics, Elsevier, vol. 57(C), pages 60-74.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. María Jose Aragon Aragon & Adriana Castelli & James Gaughan, 2017. "Hospital Trusts productivity in the English NHS: Uncovering possible drivers of productivity variations," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-14, August.
    2. Laudicella, Mauro & Li Donni, Paolo, 2021. "The impact of supply-driven variation in time to death on the demand for health care," DaCHE discussion papers 2021:3, University of Southern Denmark, Dache - Danish Centre for Health Economics.
    3. Xueyuan Wu & Chi-kin Law & Paul Siu Fai Yip, 2019. "A Projection of Future Hospitalisation Needs in a Rapidly Ageing Society: A Hong Kong Experience," IJERPH, MDPI, vol. 16(3), pages 1-11, February.
    4. Davillas, Apostolos & Pudney, Stephen, 2020. "Biomarkers, disability and health care demand," Economics & Human Biology, Elsevier, vol. 39(C).
    5. Elisabet Rodriguez Llorian & Janelle Mann, 2022. "Exploring the technology–healthcare expenditure nexus: a panel error correction approach," Empirical Economics, Springer, vol. 62(6), pages 3061-3086, June.
    6. Nigel Rice & Maria Jose Aragon, 2018. "The determinants of health care expenditure growth," Working Papers 156cherp, Centre for Health Economics, University of York.
    7. Davillas, Apostolos & Pudney, Stephen, 2020. "Using biomarkers to predict healthcare costs: Evidence from a UK household panel," Journal of Health Economics, Elsevier, vol. 73(C).
    8. Joan Costa‐Font & Cristina Vilaplana‐Prieto, 2020. "‘More than one red herring'? Heterogeneous effects of ageing on health care utilisation," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 8-29, October.
    9. Marc Carreras & Pere Ibern & José María Inoriza, 2018. "Ageing and healthcare expenditures: Exploring the role of individual health status," Health Economics, John Wiley & Sons, Ltd., vol. 27(5), pages 865-876, May.
    10. Ahmad Reshad Osmani & Albert Okunade, 2021. "A Double-Hurdle Model of Healthcare Expenditures across Income Quintiles and Family Size: New Insights from a Household Survey," JRFM, MDPI, vol. 14(6), pages 1-21, May.
    11. Jonas Krämer & Jonas Schreyögg, 2019. "Demand-side determinants of rising hospital admissions in Germany: the role of ageing," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(5), pages 715-728, July.
    12. Mauro Laudicella & Paolo Li Donni & Kim Rose Olsen & Dorte Gyrd‐Hansen, 2022. "Age, morbidity, or something else? A residual approach using microdata to measure the impact of technological progress on health care expenditure," Health Economics, John Wiley & Sons, Ltd., vol. 31(6), pages 1184-1201, June.
    13. Longden, Thomas & Wong, Chun Yee & Haywood, Philip & Hall, Jane & van Gool, Kees, 2018. "The prevalence of persistence and related health status: An analysis of persistently high healthcare costs in the short term and medium term," Social Science & Medicine, Elsevier, vol. 211(C), pages 147-156.
    14. Friedrich Breyer & Normann Lorenz, 2021. "The “red herring” after 20 years: ageing and health care expenditures," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(5), pages 661-667, July.
    15. J. Iñaki De La Peña & M. Cristina Fernández-Ramos & Asier Garayeta & Iratxe D. Martín, 2022. "Transforming Private Pensions: An Actuarial Model to Face Long-Term Costs," Mathematics, MDPI, vol. 10(7), pages 1-17, March.
    16. Lassila, Jukka & Valkonen, Tarmo, 2019. "Alternative Demography-based Projection Approaches for Public Health and Long-term Care Expenditure," ETLA Working Papers 74, The Research Institute of the Finnish Economy.
    17. Nisha C. Hazra & Caroline Rudisill & Martin C. Gulliford, 2018. "Determinants of health care costs in the senior elderly: age, comorbidity, impairment, or proximity to death?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(6), pages 831-842, July.
    18. Eric French & John Bailey Jones & Elaine Kelly & Jeremy McCauley, 2018. "End-of-Life Medical Expenses," Working Paper 18-18, Federal Reserve Bank of Richmond.
    19. Moore, Patrick V. & Bennett, Kathleen & Normand, Charles, 2017. "Counting the time lived, the time left or illness? Age, proximity to death, morbidity and prescribing expenditures," Social Science & Medicine, Elsevier, vol. 184(C), pages 1-14.
    20. Viktor von Wyl, 2019. "Proximity to death and health care expenditure increase revisited: A 15-year panel analysis of elderly persons," Health Economics Review, Springer, vol. 9(1), pages 1-16, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:468-:d:1017146. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.