MCOA: A Comprehensive Multimodal Dataset for Advancing Deep Learning in Corneal Opacity Assessment

Xinyu Ma, Jianxia Fang, Yaqi Wang, Zhichao Hu, Zhe Xu, Sha Zhu, Weijia Yan, Mengqi Chu, Jingwei Xu, Siting Sheng, Chujun Liu, Mingxuan Zhang, Ce Shi, Gangyong Jia, Wen Xu

Abstract: 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.

External IDs:doi:10.1038/s41597-025-05205-3