ArticleScientific data2025
MCOA: A Comprehensive Multimodal Dataset for Advancing Deep Learning in Corneal Opacity Assessment.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Swept-source intraoperative optical coherence tomography for real-time detection of anterior segment membranes during cataract surgery: a case series.Quantitative imaging in medicine and surgery · 2026Article
- Anterior Segment Optical Coherence Tomography with Angiography for the Cornea and Ocular Surface.Journal of clinical medicine · 2026Review
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- MCOA: A Comprehensive Multimodal Dataset for Advancing Deep Learning in Corneal Opacity Assessment.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.