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Imaging Biological Samples with Optical Microscopy

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Full-Field Optical Coherence Microscopy for Histology-Like Analysis of Stromal Features in Corneal Grafts

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Published on: October 21, 2022

Recent advances in corneal specular microscopy image analysis through artificial intelligence.

Andres G Marrugo¹, Fernando Quintero¹, Alejandro Tello^2,3,4,5

¹Escuela de Ingeniería, Arquitectura y Diseño, Universidad Tecnologica de Bolivar, Cartagena, Colombia.

PLOS Digital Health

|March 31, 2026

Summary

Artificial intelligence (AI) improves automated analysis of corneal specular microscopy images, especially with corneal guttae. AI enhances diagnostic accuracy for in vivo human corneal endothelium analysis and Fuchs endothelial corneal dystrophy.

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Published on: January 3, 2011

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Conventional automated analysis of corneal specular microscopy images faces reproducibility challenges, particularly with corneal guttae.
Recent advancements in artificial intelligence (AI) offer enhanced diagnostic potential for these challenging cases.

Purpose of the Study:

To review the integration of AI techniques in analyzing specular microscopy images.
To highlight the shift from classical to advanced AI methods for in vivo human corneal endothelium analysis.
To discuss AI-derived metrics for improved diagnostic precision, especially in Fuchs endothelial corneal dystrophy.

Main Methods:

Exploration of supervised and unsupervised learning methodologies in AI for image analysis.
Discussion of challenges in data collection, including ethical considerations and dataset quality.
Examination of novel AI-derived metrics for diagnostic enhancement.

Main Results:

AI-based methodologies significantly enhance the accuracy of in vivo human corneal endothelium analysis.
AI demonstrates potential in overcoming longstanding limitations in specular microscopy analysis.
Novel AI-derived metrics show implications for improving diagnostic precision.

Conclusions:

AI integration is increasingly relevant in ocular healthcare, particularly for specular microscopy.
AI techniques offer a promising future for overcoming diagnostic limitations in corneal analysis.
High-quality datasets and ethical considerations are crucial for advancing AI in this field.