ArticleiScience2021
Comparison of deep learning systems and cornea specialists in detecting corneal diseases from low-quality images.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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.
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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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review.Journal of advanced research · 2025Pooled it
- Deep Learning-Based Classification of Slit-Lamp Photograph Quality in Microbial Keratitis.Ophthalmology science · 2026Article
- Comparison of veterinarians and a deep learning tool in the diagnosis of equine ophthalmic diseases.Equine veterinary journal · 2025Article
- DeepMonitoring: a deep learning-based monitoring system for assessing the quality of cornea images captured by smartphones.Frontiers in cell and developmental biology · 2024Article
- Assessing the Impact of Image Quality on Deep Learning Classification of Infectious Keratitis.Ophthalmology science · 2023Article
- Artificial intelligence in ophthalmology: The path to the real-world clinic.Cell reports. Medicine · 2023Review
- Solving data quality issues of fundus images in real-world settings by ophthalmic AI.Cell reports. Medicine · 2023Article
- Artificial intelligence to detect malignant eyelid tumors from photographic images.NPJ digital medicine · 2022Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The performance of deep learning in disease detection from high-quality clinical images is identical to and even greater than that of human doctors. However, in low-quality images, deep learning performs poorly. Whether human doctors also have poor performance in low-quality images is unknown. Here, we compared the performance of deep learning systems with that of cornea specialists in detecting corneal diseases from low-quality slit lamp images. The results showed that the cornea specialists performed better than our previously established deep learning system (PEDLS) trained on only high-quality images. The performance of the system trained on both high- and low-quality images was superior to that of the PEDLS while inferior to that of a senior corneal specialist. This study highlights that cornea specialists perform better in low-quality images than the system trained on high-quality images. Adding low-quality images with sufficient diagnostic certainty to the training set can reduce this performance gap.
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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.