Kim So Yeon, Jin Jung Jong, Ha Ahnul, Song Chae Hyun, Park Se Hie, Kang Kyoung Hae, Lee Jaekyoung, Huh Min Gu, Jeoung Jin Wook, Park Ki Ho, Kim Young Kook
Department of Ophthalmology, Seoul National University Hospital.
Department of Ophthalmology, Seoul National University College of Medicine.
J Glaucoma. 2025 Jul 1;34(7):520-527. doi: 10.1097/IJG.0000000000002579. Epub 2025 Apr 21.
The AI model, enhanced by SMOTE to balance data classes, accurately predicted visual field deterioration in patients with myopic normal tension glaucoma. Using SHAP analysis, the key variables driving disease progression were identified.
To develop and validate a Synthetic Minority Over-sampling Technique (SMOTE)-enhanced artificial intelligence (AI) model for predicting visual field progression in myopic normal tension glaucoma (NTG) patients.
This retrospective cohort study included 100 eyes from myopic NTG patients with a mean follow-up of 10.3±3.2 years. Baseline parameters included intraocular pressure (IOP), central corneal thickness, axial length, and visual field metrics. A SMOTE-enhanced AI model was created to address class imbalance in progression events. Model performance was evaluated using receiver operating characteristic (ROC) analysis, cross-validation, and calibration plots. Predictive factor importance was evaluated through SHapley Additive exPlanations (SHAP) analysis.
Visual field progression was observed in 28% of patients, with a median progression time of 3.2 years. The AI model achieved an area under the ROC curve (AUC) of 0.83 (95% CI, 0.75-0.91), with promising sensitivity (0.81) and specificity (0.77). SHAP analysis identified baseline mean deviation (MD), age, axial length, baseline IOP, and visual field index (VFI) as key predictors. When patients were stratified based on model-predicted risk scores, those with scores above 0.8 had significantly higher observed progression rates (82.6%) compared with those with lower risk scores. Subgroup analysis revealed strong correlations between progression risks and older age, greater axial length, and worse baseline MD.
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.
通过SMOTE平衡数据类别增强的人工智能模型，准确预测了近视性正常眼压性青光眼患者的视野恶化情况。利用SHAP分析，确定了推动疾病进展的关键变量。
开发并验证一种经合成少数过采样技术（SMOTE）增强的人工智能（AI）模型，用于预测近视性正常眼压性青光眼（NTG）患者的视野进展。
这项回顾性队列研究纳入了100只来自近视性NTG患者的眼睛，平均随访时间为10.3±3.2年。基线参数包括眼压（IOP）、中央角膜厚度、眼轴长度和视野指标。创建了一个经SMOTE增强的AI模型来解决进展事件中的类别不平衡问题。使用受试者操作特征（ROC）分析、交叉验证和校准图评估模型性能。通过SHapley加性解释（SHAP）分析评估预测因素的重要性。
28%的患者出现视野进展，中位进展时间为3.2年。AI模型的ROC曲线下面积（AUC）为0.83（95%CI，0.75 - 0.91），具有良好的敏感性（0.81）和特异性（0.77）。SHAP分析确定基线平均偏差（MD）、年龄、眼轴长度、基线眼压和视野指数（VFI）为关键预测因素。当根据模型预测的风险评分对患者进行分层时，评分高于0.8的患者观察到的进展率（82.6%）显著高于风险评分较低的患者。亚组分析显示进展风险与年龄较大、眼轴长度较长和基线MD较差之间存在强相关性。
经SMOTE增强的AI模型在识别近视性NTG患者的视野进展方面显示出合理的预测性能和潜在的临床应用价值，不过需要在更大的队列中进一步验证。通过解决类别不平衡和近视特异性挑战，这种方法能够实现个性化风险分层和早期干预。
