Ma Xinyu, Fang Jianxia, Wang Yaqi, Hu Zhichao, Xu Zhe, Zhu Sha, Yan Weijia, Chu Mengqi, Xu Jingwei, Sheng Siting, Liu Chujun, Zhang Mingxuan, Shi Ce, Jia Gangyong, Xu Wen
Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Innovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, China.
Sci Data. 2025 May 30;12(1):911. doi: 10.1038/s41597-025-05205-3.
Corneal opacity remains a major global cause of vision impairment. Its severity is typically assessed subjectively by clinicians using slit lamp examinations of the anterior segment. While anterior segment optical coherence tomography (AS-OCT) provides high-resolution cross-sectional images of the cornea, capturing subtle structural changes, the combination of AS-OCT images with anterior segment photographs delivers a more comprehensive view of the cornea. However, the absence of large-scale, high-quality datasets hinders the development of deep learning algorithms for this purpose. To bridge this gap, we established the most extensive corneal opacity dataset available. The dataset included a total of 6,272 AS-OCT images and 392 corresponding anterior segment photographs. Each image of patients with corneal opacity was carefully annotated to include detailed cornea and corneal opacity information. This robust dataset represented a significant step forward in leveraging deep learning for corneal opacity recognition, empowering AI-driven clinical decision-making and facilitating the creation of personalized treatment plans for affected patients.
角膜混浊仍然是全球视力损害的主要原因。其严重程度通常由临床医生通过对眼前节进行裂隙灯检查主观评估。虽然眼前节光学相干断层扫描（AS-OCT）可提供角膜的高分辨率横断面图像，捕捉细微的结构变化，但将AS-OCT图像与眼前节照片相结合能提供更全面的角膜视图。然而，缺乏大规模、高质量的数据集阻碍了为此目的开发深度学习算法。为了弥补这一差距，我们建立了现有的最广泛的角膜混浊数据集。该数据集总共包括6272张AS-OCT图像和392张相应的眼前节照片。对角膜混浊患者的每张图像都进行了仔细标注，以包含详细的角膜和角膜混浊信息。这个强大的数据集代表了在利用深度学习进行角膜混浊识别方面向前迈出的重要一步，有助于人工智能驱动的临床决策，并促进为受影响患者制定个性化治疗方案。
