ReviewEye and vision (London, England)2024
Multi-modal imaging for the detection of early keratoconus: a narrative review.
Review in Eye and vision (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Early Detection of Keratoconus: Diagnostic Advances and Their Impact on Visual Outcomes: A Systematic Review.Medicina (Kaunas, Lithuania) · 2025Pooled it
- A Continuous Severity Index for Keratoconus Diagnosis Based on Kolmogorov-Arnold Networks in a Chinese Population.Ophthalmology science · 2026Article
- Enhancing the diagnostic precision of subclinical keratoconus by combining indices from Scheimpflug tomography, corneal biomechanics, and anterior segment optical coherence tomography.International journal of ophthalmology · 2026Article
- Highly efficient stacking ensemble learning model for automated keratoconus screening.Eye and vision (London, England) · 2025Article
- Application of the Brillouin Optical Scanning System in the Regional Corneal Biomechanical Evaluation of Keratoconus and Its Correlation with Corvis ST Parameters.Bioengineering (Basel, Switzerland) · 2025Article
- Strategies for Early Keratoconus Diagnosis: A Narrative Review of Evaluating Affordable and Effective Detection Techniques.Journal of clinical medicine · 2025Review
- The Combined Utilization of Epithelial Thickness and Tomographic Parameters in Keratoconus Detection.Journal of ophthalmology · 2025Article
- Advances in Corneal Diagnostics Using Machine Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Distinguishing subclinical from clinical keratoconus by corneal measurements.Frontiers in medicine · 2024Article
- Comparison of the PlusoptiX A16 and vision screener V100.Frontiers in ophthalmology · 2024Article
- Keratoconus: imaging modalities and management.Medical hypothesis, discovery & innovation ophthalmology journal · 2024Review
- Comprehensive evaluation of oculus pentacam in subclinical keratoconus and clinical keratoconus patients at different stages.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Keratoconus is a common progressive corneal disorder that can be associated with significant ocular morbidity. Various corneal imaging techniques have been used for the diagnosis of established cases. However, in the early stages of the disease, which include subclinical keratoconus and forme fruste keratoconus, detection of such cases can be challenging. The importance of detecting such cases is very important because early intervention can halt disease progression, improve visual outcomes and prevent postrefractive surgery ectasia associated with performing corneal refractive procedures in such patients. This narrative review aimed to examine several established and evolving imaging techniques for the detection of early cases of keratoconus. The utilization of combinations of these techniques may further increase their diagnostic ability.
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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.