ArticleComputational intelligence and neuroscience2019
KeratoDetect: Keratoconus Detection Algorithm Using Convolutional Neural Networks.
Article in Computational intelligence and neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 3 of them syntheses 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
59 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Early Detection of Keratoconus: Diagnostic Advances and Their Impact on Visual Outcomes: A Systematic Review.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.Frontiers in ophthalmology · 2024Pooled it
- Artificial intelligence for detecting keratoconus.The Cochrane database of systematic reviews · 2023Pooled it
- Article
- Artificial intelligence for diagnosis of keratoconus using Scheimpflug based corneal tomography.International journal of ophthalmology · 2026Article
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Ophthalmology: Practical Applications, Subspecialty Evidence and Real-World Deployment.Cureus · 2025Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Screening with the Bilateral Corneal Symmetry 3-D Analyzer.International journal of environmental research and public health · 2025Article
- Artificial intelligence virtual assistants in primary eye care practice.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025Article
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
- AI-driven strategies for advancing corneal cell therapy: a promising frontier.Frontiers in medicine · 2025Review
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- Review
- A Comprehensive Survey on Emerging Assistive Technologies for Visually Impaired Persons: Lighting the Path with Visible Light Communications and Artificial Intelligence Innovations.Sensors (Basel, Switzerland) · 2024Review
- A Beginner's Guide to Artificial Intelligence for Ophthalmologists.Ophthalmology and therapy · 2024Review
- KeratoEL: Detection of keratoconus using corneal parameters with ensemble learning.Health science reports · 2024Article
- Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach.BMJ open ophthalmology · 2024Article
- Artificial intelligence as diagnostic modality for keratoconus: A systematic review and meta-analysis.Journal of Taibah University Medical Sciences · 2024Review
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Keratoconus (KTC) is a noninflammatory disorder characterized by progressive thinning, corneal deformation, and scarring of the cornea. The pathological mechanisms of this condition have been investigated for a long time. In recent years, this disease has come to the attention of many research centers because the number of people diagnosed with keratoconus is on the rise. In this context, solutions that facilitate both the diagnostic and treatment options are quickly needed. The main contribution of this paper is the implementation of an algorithm that is able to determine whether an eye is affected or not by keratoconus. The KeratoDetect algorithm analyzes the corneal topography of the eye using a convolutional neural network (CNN) that is able to extract and learn the features of a keratoconus eye. The results show that the KeratoDetect algorithm ensures a high level of performance, obtaining an accuracy of 99.33% on the data test set. KeratoDetect can assist the ophthalmologist in rapid screening of its patients, thus reducing diagnostic errors and facilitating treatment.
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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.