VP-net: an end-to-end deep learning network for elastic wave velocity prediction in human skin in vivo using optical coherence elastography
This study indicates that VP-Net could rapidly and accurately predict elastic wave velocities related to biomechanical properties of <i>in vivo</i> healthy and abnormal skin, offering potential clinical applications in characterizing skin aging, as well as assessing and managing the treatment of acne vulgaris.
Authors
- Zhengshuyi Feng
- Jinpeng Liao (ORCID: https://orcid.org/0000-0001-6287-8079)
- Wenyue Yang (ORCID: https://orcid.org/0000-0002-0446-6567)
- Zhihong Huang (ORCID: https://orcid.org/0000-0003-4569-5400)
- Chunhui Li (ORCID: https://orcid.org/0000-0003-2186-5137)
- Yilong Zhang (ORCID: https://orcid.org/0000-0001-6750-7613)
- Zhiqiong Wang (ORCID: https://orcid.org/0000-0002-0095-0378)
- Alessandro Perelli
Institutions
- University of Dundee (GB)
- University of York (GB)
- Northeastern University (CN)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2024-10-14
- DOI
- https://doi.org/10.3389/fbioe.2024.1465823
- Citations
- 6
- Primary Topic
- Optical Coherence Tomography Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.11
Funders
- Leverhulme Trust
- Royal Academy of Engineering
- Lloyd's Tercentenary Research Foundation