ArticleBMC ophthalmology2025
Integration of tear fluid biomarkers and machine learning for the early detection of keratoconus.
Article in BMC ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- The Pro- and Anti-Inflammatory Cytokine Profile in Keratoconus as a Predictor of Five-Year Corneal Cross-Linking Outcomes.International journal of molecular sciences · 2026Observational
- Imbalance of Pro-Oxidant and Anti-Oxidant Biomarkers in Patients with Keratoconus.Antioxidants (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundKeratoconus (KC) is a progressive corneal disorder that leads to visual impairment owing to corneal thinning and deformation. While current diagnostic methods are effective in the later stages, early detection of KC remains challenging. This study aimed to investigate the potential of tear fluid biomarkers combined with machine learning (ML) models for the early diagnosis of KC.
methodsA total of 370 participants were recruited, including 134 patients with KC, 93 with early KC, and 143 normal controls. Cytokine levels in tear fluid were measured using multiplex cytokine analysis. Clinical parameters were evaluated, and nine ML models were employed to predict early KC risk based on tear biomarkers and clinical data.
resultsSignificant increases in IL-1β, Galectin-1, and Galectin-3 levels were observed in the KC and early KC groups compared to controls (p < 0.05 for all). The Random Forest model demonstrated the highest accuracy when integrating tear biomarker levels and clinical parameters. Key biomarkers, such as Galectin-3, IL-1β, and Galectin-1, in combination with clinical parameters (D-index, BE, and Kmax), significantly enhanced prediction accuracy. Furthermore, a web application was developed to facilitate clinical deployment, enabling personalized risk assessment for early KC.
conclusionsThe study highlight the potential of integrating noninvasive tear fluid biomarkers, Pentacam parameters and ML techniques to improve the early detection of KC.
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