SMOTE-Enhanced Explainable Artificial Intelligence Model for Predicting Visual Field Progression in Myopic Normal Tension Glaucoma
The SMOTE-enhanced AI model shows reasonable predictive performance and potential clinical utility for identifying visual field progression in myopic NTG patients, though further validation in larger cohorts is needed. By addressing class imbalance and myopia-specific challenges, this approach enables personalized risk stratification and early intervention.
Authors
- Se Hie Park
- Chae Hyun Song
- Jin Wook Jeoung (ORCID: https://orcid.org/0000-0002-1690-1239)
- Ahnul Ha (ORCID: https://orcid.org/0000-0003-4211-7971)
- Min Gu Huh (ORCID: https://orcid.org/0009-0008-3006-2217)
- Kyoung Hae Kang
- Young Kook Kim (ORCID: https://orcid.org/0000-0002-6037-8449)
- Ki Ho Park (ORCID: https://orcid.org/0000-0002-8137-8843)
- Jianmin Jin (ORCID: https://orcid.org/0000-0001-9558-4587)
- Jaekyoung Lee (ORCID: https://orcid.org/0000-0002-2071-3097)
- So Yeon Kim
Institutions
- Seoul National University (KR)
- New Generation University College (ET)
- Seoul National University Hospital (KR)
- Kim's Eye Hospital (KR)
- Yeungnam University Medical Center (KR)
- Jeju National University Hospital (KR)
- Jeju National University (KR)
Publication Details
- Journal
- Journal of Glaucoma
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1097/ijg.0000000000002579
- Citations
- 3
- Primary Topic
- Glaucoma and retinal disorders
- Type
- article
- Field-Weighted Citation Impact
- 5.12