ArticleTranslational vision science & technology2021
Detection of Subclinical Keratoconus With a Validated Alternative Method to Corneal Densitometry.
Article in Translational vision science & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Influence of Room Illumination on Corneal Densitometry.Translational vision science & technology · 2026Article
- Combination of tissue-derived and shape-based parameters for subclinical keratoconus detection.Biomedical optics express · 2026Article
- A novel optical sign in keratoconus: the Halo Sign and its clinical characteristics.BMC ophthalmology · 2026Observational
- Deep learning-based keratoconus detection from Scheimpflug images.Biomedical optics express · 2025Article
- Strategies for Early Keratoconus Diagnosis: A Narrative Review of Evaluating Affordable and Effective Detection Techniques.Journal of clinical medicine · 2025Review
- Diagnosis and management of keratoconus in the paediatric age group: a review of current evidence.Eye (London, England) · 2023Review
- Corneal tissue changes following short-term soft contact lens wear of different materials.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2023Article
- Influence of eye tilt on corneal densitometry.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2022Article
- Suspect glaucoma detection from corneal densitometry supported by machine learning.Journal of optometry · 2022Article
- 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
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To enhance the current standards of subclinical keratoconus screening based on the statistical modeling of the pixel intensity distribution of Scheimpflug images. Methods: Scheimpflug corneal tomographies corresponding to 25 corneal meridians of 60 participants were retrospectively collected and divided into three groups: controls (20 eyes), subclinical keratoconus (20 eyes), and clinical keratoconus (20 eyes). Only right eyes were selected. After corneal segmentation, pixel intensities of the stromal tissue were statistically modeled using a Weibull probability density function from which parameter α (pixel brightness) was derived. Further, data were transformed to polar coordinates, smoothed, and interpolated to build a map of the corneal α parameter. The discriminative power of the method was analyzed using receiver operating characteristic curves. Results: The proposed platform-independent method achieved a higher performance in discriminating subclinical keratoconus from control eyes (90.0% sensitivity, 95.0% specificity, 0.97 area under the curve [AUC]) than the standard method (Belin-Ambrósio enhanced ectasia display), which uses only corneal morphometry (85.0% sensitivity, 85.0% specificity, 0.80 AUC). Conclusions: Analysis of light backscatter at the cornea successfully discriminates subclinical keratoconus from control eyes, upgrading the results previously reported in the literature. Translational Relevance: The proposed methodology has the potential to support clinicians in the detection of keratoconus before showing clinical signs.
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