Institute of Artificial Intelligence, Xiamen University, Xiamen, Fujian, China.
Xiamen University affiliated Xiamen Eye Center; Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Fujian Engineering and Research Center of Eye Regenerative Medicine, Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
Invest Ophthalmol Vis Sci. 2023 Oct 3;64(13):7. doi: 10.1167/iovs.64.13.7.
Accurate quantification measurement of tear meniscus is vital for the precise diagnosis of dry eye. In current clinical practice, the measurement of tear meniscus height (TMH) relies on doctors' manual operation. This study aims to propose a novel automatic artificial intelligence (AI) system to evaluate TMH.
A total of 510 photographs obtained by the oculus camera were labeled. Three thousand and five hundred images were finally attained by data enhancement to train the neural network model parameters, and 60 were used to evaluate the model performance in segmenting the cornea and tear meniscus region. One hundred images were used to test generalization ability of the model. We modified a segmentation model of the cornea and the tear meniscus based on the UNet-like network. The output of the segmentation model is followed by a calculation module that calculates and reports the TMH.
Compared with ground truth (GT) manually labeled by clinicians, our modified model achieved a Dice Similarity Coefficient (DSC) and Intersection over union (Iou) of 0.99/0.98 in the corneal segmentation task and 0.92/0.86 for the detection of tear meniscus on the validation set, respectively. On the test set, the TMH automatically measured by our AI system strongly correlates with the results manually calculated by the ophthalmologists.
We developed a fully automated and reliable AI system to obtain TMH. After large-scale clinical testing, our method could be used for dry eye screening in clinical practice.
准确测量泪膜弯月高对于干眼的精确诊断至关重要。在当前的临床实践中，泪膜弯月高（TMH）的测量依赖于医生的手动操作。本研究旨在提出一种新的自动人工智能（AI）系统来评估 TMH。
对 oculus 相机获得的 510 张照片进行标注。通过数据增强最终获得 3500 张图像来训练神经网络模型参数，并用 60 张图像评估模型对角膜和泪膜区域进行分割的性能。用 100 张图像测试模型的泛化能力。我们基于 UNet 样网络对角膜和泪膜分割模型进行了修改。分割模型的输出后面是一个计算模块，用于计算和报告 TMH。
与由临床医生手动标注的地面实况（GT）相比，我们的改进模型在角膜分割任务中的 Dice 相似系数（DSC）和交并比（IoU）分别达到 0.99/0.98，在验证集上检测泪膜弯月高的分别达到 0.92/0.86。在测试集上，我们的 AI 系统自动测量的 TMH 与眼科医生手动计算的结果具有很强的相关性。
我们开发了一种全自动、可靠的 AI 系统来获取 TMH。经过大规模的临床测试，我们的方法可用于临床实践中的干眼病筛查。
