EyeCV: A Novel Tool for Rapid, Accurate, and Reproducible Screening of Endothelial Cell Morphology in Diseased and Healthy Corneas

Abstract

Researchers at the University of California, Davis have developed a machine learning-based platform for early, accurate, and efficient detection of endothelial disease from corneal images assessment.

Full Description

This technology is a machine learning solution that efficiently evaluates the morphological features of endothelial cells. The technology includes a custom dataset for training the model, segmentation based on deep learning, and a comprehensive analysis report to assist veterinarians. It serves as a diagnostic tool, removing subjectivity from endothelial cell evaluations and supporting the assessment of treatment outcomes.

Applications

• Diagnostic tool for detecting endothelial cell diseases in animals. 
• Platform for tracking disease progression and treatment effectiveness. 
• Clinical decision support tool for veterinary care. 
• Potential application in human subjects with appropriate data.

Features/Benefits

• Fast and accurate evaluation of endothelial cells. 
• Exceptional dataset for training the model. 
• Productive platform to assess the efficiency of treatment protocols. 
• Offers better clinical decision support in veterinary care. 
• Mitigates time-consuming manual methods for assessing corneal endothelial cells. 
• Addresses subjectivity and variability in endothelial cell evaluations. 
• Solves difficulty in tracking disease progression and measuring the impact of treatments.

Patent Status

Patent Pending