AI-driven Eyeball Exposure Rate (EER) analysis: A useful tool for assessing ptosis surgery effectiveness
AI-based EER analysis is accurate and efficient, providing comparable results to manual methods. Its ability to simplify surgical outcome assessments makes it a promising addition to clinical practice. Further exploration of AI in evaluating three-dimensional changes in ptosis surgery could enhance future surgical assessments and outcomes.
Authors
- Sun Je Kim (ORCID: https://orcid.org/0000-0001-7887-7521)
- Yooseok Ha (ORCID: https://orcid.org/0000-0001-7843-5790)
- Chang Wook Seo (ORCID: https://orcid.org/0000-0002-3809-9515)
- Seung Han Song (ORCID: https://orcid.org/0000-0001-5996-3408)
- B. C. Lee
- Kyu Cheol Lee
- Hyeokjae Kwon (ORCID: https://orcid.org/0000-0002-1418-3448)
- Sang‐Ha Oh (ORCID: https://orcid.org/0000-0003-3734-5005)
- Soo Yeon Kim (ORCID: https://orcid.org/0000-0003-2240-3647)
- Lianji Xu
Institutions
- Beijing Tongren Hospital (CN)
- Capital Medical University (CN)
- Chungnam National University (KR)
- Cheongju St. Mary's Hospital (KR)
- Chungnam National University Hospital (KR)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-03-25
- DOI
- https://doi.org/10.1371/journal.pone.0319577
- Citations
- 1
- Primary Topic
- Facial Rejuvenation and Surgery Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.00
Funders
- Ministry of Science and ICT, South Korea