ArticleOphthalmology2022
Differentiation of Active Corneal Infections from Healed Scars Using Deep Learning.
Article in Ophthalmology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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.
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
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysis.Frontiers in public health · 2023Pooled it
- Evaluation of the clinical efficacy of accelerated photoactivated chromophore for keratitis-CXL for early to mid-stage fungal keratitis: a prospective, multicenter randomized controlled trial.International ophthalmology · 2026Trial
- Validated semi-supervised early and accurate screening for anterior segment diseases: a 3PM-guided conceptual and technological innovation.The EPMA journal · 2026Article
- Artificial Intelligence for Diagnostic Guidance in Ocular Surface Disorders.Journal of clinical medicine · 2026Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- SLID: a slit-lamp image dataset for deep learning-based anterior eye anatomical segmentation and multi-lesion detection.Frontiers in digital health · 2025Article
- Artificial intelligence in stroke risk assessment and management via retinal imaging.Frontiers in computational neuroscience · 2025Review
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- Diagnostic performance of deep learning for infectious keratitis: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Establishment of a corneal ulcer prognostic model based on machine learning.Scientific reports · 2024Article
- Application of Deep Learning Algorithms Based on the Multilayer Y0L0v8 Neural Network to Identify Fungal Keratitis.Sovremennye tekhnologii v meditsine · 2024Article
- Monitoring the Progression of Clinically Suspected Microbial Keratitis Using Convolutional Neural Networks.Translational vision science & technology · 2023Article
- Artificial Intelligence and Infectious Keratitis: Where Are We Now?Life (Basel, Switzerland) · 2023Review
- Diagnosis ofDiagnostics (Basel, Switzerland) · 2023Review
- Artificial intelligence in ophthalmology: The path to the real-world clinic.Cell reports. Medicine · 2023Review
- Diagnostic performance of deep learning in infectious keratitis: a systematic review and meta-analysis protocol.BMJ open · 2023Article
- Artificial intelligence-assisted diagnosis of ocular surface diseases.Frontiers in cell and developmental biology · 2023Review
- Big data in corneal diseases and cataract: Current applications and future directions.Frontiers in big data · 2023Review
- Artificial intelligence and corneal diseases.Current opinion in ophthalmology · 2022Review
- Infectious keratitis: A review.Clinical & experimental ophthalmology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 5 institutions in 2 countries.
Funding
Abstract
purposeTo develop and evaluate an automated, portable algorithm to differentiate active corneal ulcers from healed scars using only external photographs.
designA convolutional neural network was trained and tested using photographs of corneal ulcers and scars.
participantsDe-identified photographs of corneal ulcers were obtained from the Steroids for Corneal Ulcers Trial (SCUT), Mycotic Ulcer Treatment Trial (MUTT), and Byers Eye Institute at Stanford University.
methodsPhotographs of corneal ulcers (n = 1313) and scars (n = 1132) from the SCUT and MUTT were used to train a convolutional neural network (CNN). The CNN was tested on 2 different patient populations from eye clinics in India (n = 200) and the Byers Eye Institute at Stanford University (n = 101). Accuracy was evaluated against gold standard clinical classifications. Feature importances for the trained model were visualized using gradient-weighted class activation mapping.
main outcome measuresAccuracy of the CNN was assessed via F
resultsThe CNN correctly classified 115 of 123 active ulcers and 65 of 77 scars in patients with corneal ulcer from India (F
conclusionsThe CNN classified corneal ulcers and scars with high accuracy and generalized to patient populations outside of its training data. The CNN focused on clinically relevant features when it made a diagnosis. The CNN demonstrated potential as an inexpensive diagnostic approach that may aid triage in communities with limited access to eye care.
Indexed as
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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.