Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, 251 Fukang Road, Tianjin, 300384, China.
BMC Ophthalmol. 2023 Aug 21;23(1):361. doi: 10.1186/s12886-023-03079-w.
To develop a deep learning (DL) model based on preoperative optical coherence tomography (OCT) training to automatically predict the 6-month postoperative visual outcomes in patients with idiopathic epiretinal membrane (iERM).
In this retrospective cohort study, a total of 442 eyes (5304 images in total) were enrolled for the development of the DL and multimodal deep fusion network (MDFN) models. All eyes were randomized into a training dataset with 265 eyes (60.0%), a validation dataset with 89 eyes (20.1%), and an internal testing dataset with the remaining 88 eyes (19.9%). The input variables for prediction consisted of macular OCT images and diverse clinical data. Inception-Resnet-v2 network was utilized to estimate the 6-month postoperative best-corrected visual acuity (BCVA). Concurrently, a regression model was developed using the clinical data and OCT parameters in the training data set for predicting postoperative BCVA. The reliability of the models was subsequently evaluated using the testing dataset.
The prediction DL algorithm exhibited a mean absolute error (MAE) of 0.070 logMAR and root mean square error (RMSE) of 0.11 logMAR in the testing dataset. The DL model demonstrated a robust promising performance with R = 0.80, notably superior to R = 0.49 of the regression model. The percentages of BCVA prediction errors within ± 0.20 logMAR amounted to 94.32% in the testing dataset.
The OCT-based DL model demonstrated sensitivity and accuracy in predicting postoperative BCVA in iERM patients. This innovative DL model exhibits substantial potential for integration into surgical planning protocols.
开发一种基于术前光学相干断层扫描（OCT）训练的深度学习（DL）模型，以自动预测特发性视网膜内膜（iERM）患者术后 6 个月的视觉结果。
在这项回顾性队列研究中，共纳入 442 只眼（共 5304 张图像）用于开发 DL 和多模态深度融合网络（MDFN）模型。所有眼睛随机分为训练数据集（265 只眼，占 60.0%）、验证数据集（89 只眼，占 20.1%）和内部测试数据集（其余 88 只眼，占 19.9%）。预测的输入变量包括黄斑 OCT 图像和各种临床数据。使用 Inception-Resnet-v2 网络来估计术后 6 个月的最佳矫正视力（BCVA）。同时，在训练数据集的临床数据和 OCT 参数的基础上开发回归模型，用于预测术后 BCVA。随后使用测试数据集评估模型的可靠性。
在测试数据集，预测 DL 算法的平均绝对误差（MAE）为 0.070 logMAR，均方根误差（RMSE）为 0.11 logMAR。DL 模型表现出稳健的良好性能，R=0.80，明显优于回归模型的 R=0.49。测试数据集 BCVA 预测误差在±0.20 logMAR 内的百分比为 94.32%。
基于 OCT 的 DL 模型在预测 iERM 患者术后 BCVA 方面表现出敏感性和准确性。这种创新的 DL 模型具有整合到手术规划方案中的巨大潜力。
