EVO-ICL Vault Prediction: A Data Wrangling Framework Integrating Multicenter Big Data and Machine Learning
The development of DVIS is valuable for ICL vault prediction models, as it provides a data wrangling strategy that improves ML efficiency. Experimental results confirm the applicability of this synergistic method in enhancing existing ML approaches for ICL vault prediction, thereby facilitating more informed clinical decision-making in ICL implantation surgery.
Authors
- Hongbin Lin (ORCID: https://orcid.org/0000-0002-4057-7360)
- Guoliang Fan (ORCID: https://orcid.org/0000-0002-9655-7545)
- Ling Ling (ORCID: https://orcid.org/0000-0001-7373-8557)
- Wentian Zhou (ORCID: https://orcid.org/0000-0001-5907-5427)
- Quan Liu (ORCID: https://orcid.org/0000-0002-4958-2051)
- Feng Zhu (ORCID: https://orcid.org/0000-0001-9175-3718)
- Yongjin Li (ORCID: https://orcid.org/0000-0003-4322-308X)
- Xiaoli Li (ORCID: https://orcid.org/0000-0001-9113-7130)
- Yi Liu (ORCID: https://orcid.org/0000-0003-1288-1071)
- Zhen Qiu (ORCID: https://orcid.org/0000-0001-8485-3243)
- Mingkui Tan
- Xiaoqin Su
- Chao Liao
- Zonghui Yan
Institutions
- Sun Yat-sen University (CN)
- Kunming Third People's Hospital (CN)
- ShenZhen People’s Hospital (CN)
- Third Hospital of Nanchang (CN)
- Third People's Hospital of Hefei (CN)
- Guangzhou Eighth People's Hospital (CN)
- Hefei First People's Hospital (CN)
- South China University of Technology (CN)
Publication Details
- Journal
- Ophthalmology and Therapy
- Published
- 2025-12-09
- DOI
- https://doi.org/10.1007/s40123-025-01288-8
- Primary Topic
- Knee injuries and reconstruction techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00