Zhang Weixiao, Rong Hua, Hei Kaiwen, Liu Guihua, He Meinan, Du Bei, Wei Ruihua, Zhang Yan
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, Tianjin, China.
Front Bioeng Biotechnol. 2025 Apr 11;13:1554432. doi: 10.3389/fbioe.2025.1554432. eCollection 2025.
Modalities for myopia control, such as orthokeratology, repeated low-intensity red light (RLRL) treatment, and low-concentration atropine, have become popular topics. However, the effects of these three modalities on ocular surface health remain unclear. The tear meniscus height (TMH), a crucial criterion for evaluating ocular surface health and diagnosing dry eye, is conventionally measured via manual demarcation of ocular surface images, which is inefficient and involves subjective judgment. Therefore, this study sought to establish a deep learning model for automatic TMH measurement on ocular surface images to improve the efficiency and accuracy of the initial screening of dry eye associated with myopia control modalities.
To establish a model, 1,200 ocular surface images captured with an OCULUS Keratograph 5M were collected. The tear meniscus area on the image was initially marked by one experienced ophthalmologist and verified by the other. The whole image dataset was divided into a training set (70%), a validation set (20%), a test set (10%), and an external validation set (100 ocular surface images) for model construction. The deep learning model was applied to ocular surface imaging data from previous clinical trials using orthokeratology, RLRL therapy, and 0.01% atropine for myopia control. TMHs at follow-ups were automatically measured by the deep learning model.
Two hundred training iterations were performed to establish the model. At the 124th iteration, the IoU of the validation set peaked at 0.913, and the parameters of the model were saved for the testing process. The model IoU was 0.928 during testing. The AUC of the ROC curve was 0.935, and the R2 of the linear regression analysis was 0.92. The good performance and comprehensive validation of the model warrants its application to automatic TMH measurement in clinical trials of myopia control. There were no significant changes in the TMH during the follow-up period after treatment with orthokeratology, RLRL, or 0.01% atropine.
A deep learning model was established for automatic measurement of the TMH on Keratograph 5M-captured ocular surface images. This model demonstrated high accuracy, great consistency with manual measurements, and applicability to the initial screening of dry eye associated with myopia control modalities.
角膜塑形术、重复低强度红光（RLRL）治疗和低浓度阿托品等近视控制方法已成为热门话题。然而，这三种方法对眼表健康的影响仍不明确。泪膜弯月面高度（TMH）是评估眼表健康和诊断干眼的关键指标，传统上是通过手动划定眼表图像来测量的，效率低下且涉及主观判断。因此，本研究旨在建立一种深度学习模型，用于自动测量眼表图像上的TMH，以提高与近视控制方法相关的干眼初步筛查的效率和准确性。
为建立模型，收集了用OCULUS Keratograph 5M采集的1200张眼表图像。图像上的泪膜弯月面区域最初由一位经验丰富的眼科医生标记，并由另一位医生进行验证。整个图像数据集被分为训练集（70%）、验证集（20%）、测试集（10%）和外部验证集（100张眼表图像）用于模型构建。将深度学习模型应用于先前使用角膜塑形术、RLRL治疗和0.01%阿托品进行近视控制的临床试验的眼表成像数据。随访时的TMH由深度学习模型自动测量。
进行了200次训练迭代以建立模型。在第124次迭代时，验证集的交并比（IoU）达到峰值0.913，并保存模型参数用于测试过程。测试期间模型的IoU为0.928。ROC曲线的AUC为0.935，线性回归分析的R2为0.92。该模型的良好性能和全面验证保证了其在近视控制临床试验中用于自动测量TMH的应用。角膜塑形术、RLRL或0.01%阿托品治疗后的随访期间，TMH没有显著变化。
建立了一种深度学习模型，用于自动测量Keratograph 5M采集的眼表图像上的TMH。该模型显示出高准确性，与手动测量具有高度一致性，并且适用于与近视控制方法相关的干眼初步筛查。
