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Innovative Algorithm for Keratoconus Intelligent Grading Using Variational Encoding Bayesian Gaussian Mixture Model
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DOI：10.1016/j.bspc.2026.109642.png)
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Abstract
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The variational encoding Bayesian Gaussian mixture model is a novel unsupervised machine learning model that combines variational encoding and Bayesian inference. The model uses a variational encoder to learn the continuous, structured latent space of a dataset and combines multiple Gaussian mixture distributions, offering robust learning and generalisation capabilities. Although it is well-suited for modelling complex relationships, this model has not yet been explored in grading keratoconus, a blinding eye disease with unclear etiology or standardised diagnostic treatment systems. Therefore, this study applies the Bayesian Gaussian mixture model to categorise keratoconus severity and identifies relatively sensitive features for early diagnoses and interventions, potentially improving clinical decision-making and visual outcomes.
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Combined biomechanical and tomographic keratoconus staging: Adding a biomechanical parameter to the ABCD keratoconus staging system.png)
ACTA OPHTHALMOLOGICA
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ACTA OPHTHALMOLOGICAIF2.8
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ShareSaveThe Prevalence and Risk Factors for Keratoconus: A Systematic Review and Meta-AnalysisCornea
CorneaIF0
PREAI
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