Innovative Algorithm for Keratoconus Intelligent Grading Using Variational Encoding Bayesian Gaussian Mixture Model

Zuoping Tan

doi:10.1016/j.bspc.2026.109642

Innovative Algorithm for Keratoconus Intelligent Grading Using Variational Encoding Bayesian Gaussian Mixture Model

2026-01-23

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DOI：10.1016/j.bspc.2026.109642

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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.

Innovative Algorithm for Keratoconus Intelligent Grading Using Variational Encoding Bayesian Gaussian Mixture Model

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