Department of Ophthalmology, Mie University Graduate School of Medicine, Tsu, Mie, Japan.
Department of Electrical and Electronic Engineering, Mie University, Tsu, Mie, Japan.
PLoS One. 2024 Jul 22;19(7):e0304281. doi: 10.1371/journal.pone.0304281. eCollection 2024.
The purpose of this study was to develop a model that can predict the postoperative visual acuity in eyes that had undergone vitrectomy for an epiretinal membrane (ERM). The Light Gradient Boosting Machine (LightGBM) was used to evaluate the accuracy of the prediction and the contribution of the explanatory variables. Two models were designed to predict the postoperative visual acuity in 67 ERM patients. Model 1 used the age, sex, affected eye, axial length, preoperative visual acuity, Govetto's classification stage, and OCT-derived vector information as features to predict the visual acuity at 1, 3, and 6 months postoperatively. Model 2 incorporated the early postoperative visual acuity as an additional variable to predict the visual acuity at 3, and 6 months postoperatively. LightGBM with 100 iterations of 5-fold cross-validation was used to tune the hyperparameters and train the model. This involved addressing multicollinearity and selecting the explanatory variables. The generalized performance of these models was evaluated using the root mean squared error (RMSE) in a 5-fold cross-validation, and the contributions of the explanatory variables were visualized using the average Shapley Additive exPlanations (SHAP) values.
The RMSEs for the predicted visual acuity of Model 1 were 0.14 ± 0.02 logMAR units at 1 month, 0.12 ± 0.03 logMAR units at 3 months, and 0.13 ± 0.04 logMAR units at 6 months. High SHAP values were observed for the preoperative visual acuity and the ectopic inner foveal layer (EIFL) area with significant and positive correlations across all models. Model 2 that incorporated the postoperative visual acuity was used to predict the visual acuity at 3 and 6 months, and it had superior accuracy with RMSEs of 0.10 ± 0.02 logMAR units at 3 months and 0.10 ± 0.04 logMAR units at 6 months. High SHAP values were observed for the postoperative visual acuity in Model 2.
Predicting the postoperative visual acuity in ERM patients is possible using the preoperative clinical data and OCT images with LightGBM. The contribution of the explanatory variables can be visualized using the SHAP values, and the accuracy of the prediction models improved when the postoperative visual acuity is included as an explanatory variable. Our data-driven machine learning models reveal that preoperative visual acuity and the size of the EIFL significantly influence postoperative visual acuity. Early intervention may be crucial for achieving favorable visual outcomes in eyes with an ERM.
本研究旨在开发一种模型，用于预测接受眼内膜切除术（ERM）治疗后的眼术后视力。使用 Light Gradient Boosting Machine（LightGBM）评估预测的准确性和解释变量的贡献。设计了两个模型来预测 67 例 ERM 患者的术后视力。模型 1 使用年龄、性别、受影响的眼睛、眼轴长度、术前视力、Govetto 分类阶段和 OCT 衍生的向量信息作为特征，预测术后 1、3 和 6 个月的视力。模型 2 将术后早期视力作为附加变量纳入，以预测术后 3 和 6 个月的视力。使用 5 倍交叉验证的 100 次迭代的 LightGBM 来调整超参数并训练模型。这涉及解决多重共线性并选择解释变量。使用 5 倍交叉验证中的均方根误差（RMSE）评估这些模型的综合性能，并使用平均 Shapley Additive exPlanations（SHAP）值可视化解释变量的贡献。
模型 1 预测的术后视力的 RMSE 分别为术后 1 个月时为 0.14 ± 0.02 logMAR 单位，术后 3 个月时为 0.12 ± 0.03 logMAR 单位，术后 6 个月时为 0.13 ± 0.04 logMAR 单位。术前视力和异位内黄斑层（EIFL）面积的 SHAP 值较高，在所有模型中均具有显著正相关关系。纳入术后视力的模型 2 用于预测术后 3 个月和 6 个月的视力，其 RMSE 分别为 0.10 ± 0.02 logMAR 单位和 0.10 ± 0.04 logMAR 单位，具有更高的准确性。模型 2 中观察到术后视力的 SHAP 值较高。
使用 LightGBM 可以使用术前临床数据和 OCT 图像预测 ERM 患者的术后视力。可以使用 SHAP 值可视化解释变量的贡献，并且当将术后视力作为解释变量纳入时，预测模型的准确性会提高。我们的数据驱动机器学习模型表明，术前视力和 EIFL 的大小对术后视力有显著影响。早期干预对于 ERM 眼中获得良好的视力结果可能至关重要。
