A Deep Learning-Based Automated Scoring System for Predicting Eyelid Rejuvenation Outcomes After Monopolar Radiofrequency Treatment
This proof-of-concept study demonstrates the feasibility of an automated deep learning-based scoring system for eyelid MRF outcomes. By providing objective, consistent, and reproducible evaluations, the system has the potential to enhance patient counseling, guide individualized treatment planning, and enable standardized research comparisons across clinics. Larger and more diverse datasets with longer follow-up are needed for further validation.
Authors
- Byung Chul Jang (ORCID: https://orcid.org/0000-0002-2254-7918)
- Hei Sung Kim (ORCID: https://orcid.org/0000-0003-0576-0474)
- Sang Jun Lee (ORCID: https://orcid.org/0000-0001-9498-0254)
- In Kim (ORCID: https://orcid.org/0000-0001-7211-5710)
- Dong Hye Suh
Institutions
- Kyungpook National University (KR)
- The Catholic University of Korea Incheon St. Mary's Hospital (KR)
- Aesthetic Surgery Center (US)
- University Dermatology (US)
- Catholic University of Korea (KR)
Publication Details
- Journal
- Clinical Cosmetic and Investigational Dermatology
- Published
- 2025-11-01
- DOI
- https://doi.org/10.2147/ccid.s550147
- Primary Topic
- Facial Rejuvenation and Surgery Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- American Academy of Dermatology
- National Research Foundation
- Catholic University of Korea
- Korea Health Industry Development Institute
- National Research Foundation of Korea
- Incheon St. Mary's Hospital, Catholic University of Korea