School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, 100069, China.
Int Ophthalmol. 2023 Mar;43(3):837-845. doi: 10.1007/s10792-022-02485-1. Epub 2022 Sep 9.
To investigate the effect of comprehensive factor analysis on the relationship between glaucoma assessment and combined parameters including trans-laminar cribrosa pressure difference (TLCPD) and fractional pressure reserve (FPR).
The clinical data of 1029 patients with 15 indicators from the medical records of Beijing Tongren Hospital and 600 cases with 1322 indicators from Beijing Eye Research were collected. The doc2vec method was used to vectorize. The multivariate imputation by chained equations (MICE) method was used to interpolate. The original data combined with TLCPD, combined with FPR, and not combined parameters were respectively applied to train the neural network based on VGG16 and autoencoder to predict glaucoma and to evaluate the effect of combined parameters.
The accuracy rates used to classify the glaucoma of the two sets reach over 0.90, and the precision rates reach 0.70 and 0.80 respectively. After using TLCPD and FPR for the autoencoder method, the accuracy rates are both close to 1.0, and the precision rates are 0.90 and 0.70 respectively.
Using the combined parameters of FPR and TLCPD can effectively improve the diagnosis and prediction of glaucoma. Compared with TLCPD, FPR is more suitable for improving the effect of neural network for glaucoma classification.
探讨综合因素分析对青光眼评估与包括跨层筛板压力差（TLCPD）和分数压力储备（FPR）在内的综合参数之间关系的影响。
收集了北京同仁医院病历中的 1029 例患者的 15 项指标和北京眼研究中的 600 例患者的 1322 项指标的临床数据。采用 doc2vec 方法进行向量表示。采用链式方程多元插补（MICE）方法进行插补。将原始数据与 TLCPD、与 FPR 相结合以及未结合参数分别应用于基于 VGG16 和自动编码器的神经网络训练，以预测青光眼并评估组合参数的效果。
两组用于分类青光眼的准确率均超过 0.90，且精度率分别达到 0.70 和 0.80。在使用 TLCPD 和 FPR 对自动编码器方法进行后，准确率均接近 1.0，且精度率分别为 0.90 和 0.70。
使用 FPR 和 TLCPD 的联合参数可有效提高青光眼的诊断和预测效果。与 TLCPD 相比，FPR 更适合提高神经网络对青光眼分类的效果。
