Liang Chunlan, Liu Lian, Zhao Tianqi, Ouyang Weiyun, Yu Guocheng, Lyu Jun, Zhong Jingxiang
Department of Ophthalmology, The First Affiliated Hospital of Jinan University, 613 Huangpu Road, Guangzhou, 510630, Guangdong Province, China.
Department of Clinical Research, The First Affiliated Hospital of Jinan University, 613 Huangpu Road, Guangzhou, 510630, Guangdong Province, China.
J Med Syst. 2025 Apr 29;49(1):57. doi: 10.1007/s10916-025-02190-3.
Accurate prediction of post-treatment visual acuity in macular edema secondary to retinal vein occlusion (RVO-ME) is critical for optimizing anti-VEGF therapy and improving clinical outcomes. While machine learning (ML) has shown promise in ophthalmic prognostication, existing models often lack interpretability and clinical applicability for RVO management. This study developed and validated an interpretable ML model to predict visual acuity changes in RVO patients following anti-VEGF treatment. Using retrospective data from 259 RVO patients at the First Affiliated Hospital of Jinan University, we identified key predictive features through the Boruta algorithm and evaluated eight ML algorithms. The Extreme Gradient Boosting (XGBoost) model emerged as optimal, achieving an AUC of 0.91 (95% CI: 0.85-0.96) in the testing cohort with 0.83 accuracy, 0.88 sensitivity, 0.73 specificity, 0.87 F1 score, and 0.14 Brier score. Critical predictors included baseline visual acuity, systolic blood pressure (SBP), age, diabetic retinal inner layer dysfunction (DRIL), and disease subtype. Shapley Additive exPlanations (SHAP) analysis revealed baseline visual acuity as the most influential prognostic factor, followed by SBP and age. Our model seeks to bridge the critical gaps in current research: (1) systematically comparing the applicability and effects of different ML algorithms in RVO-ME visual acuity prediction, and (2) inherent interpretability through SHAP value visualization. The combination of high predictive performance (AUC > 0.9) with inherent clinical transparency may enable the practical implementation of this tool in guiding anti-VEGF treatment decisions. Future validation in multicenter cohorts could further strengthen its generalizability for personalized RVO management.
准确预测视网膜静脉阻塞继发黄斑水肿（RVO - ME）治疗后的视力对于优化抗VEGF治疗和改善临床结局至关重要。虽然机器学习（ML）在眼科预后评估中显示出前景，但现有模型在RVO管理方面往往缺乏可解释性和临床适用性。本研究开发并验证了一种可解释的ML模型，以预测RVO患者抗VEGF治疗后的视力变化。利用暨南大学附属第一医院259例RVO患者的回顾性数据，我们通过Boruta算法确定了关键预测特征，并评估了八种ML算法。极端梯度提升（XGBoost）模型表现最优，在测试队列中AUC为0.91（95%CI：0.85 - 0.96），准确率为0.83，灵敏度为0.88，特异度为0.73，F1分数为0.87，布里尔分数为0.14。关键预测因素包括基线视力、收缩压（SBP）、年龄、糖尿病性视网膜内层功能障碍（DRIL）和疾病亚型。Shapley值相加解释（SHAP）分析显示基线视力是最有影响力的预后因素，其次是SBP和年龄。我们的模型旨在弥合当前研究中的关键差距：（1）系统比较不同ML算法在RVO - ME视力预测中的适用性和效果，以及（2）通过SHAP值可视化实现内在可解释性。高预测性能（AUC > 0.9）与内在临床透明度的结合可能使该工具在指导抗VEGF治疗决策中得以实际应用。未来在多中心队列中的验证可以进一步加强其在个性化RVO管理中的通用性。
