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

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Artificial intelligence applications in different imaging modalities for corneal topography.

S Shanthi¹, Lokeshwari Aruljyothi², Manohar Babu Balasundaram²

¹Kongu Engineering College, Erode, Tamil Nadu, India.

Survey of Ophthalmology

|August 27, 2021

Summary

Artificial intelligence (AI) shows promise in interpreting corneal topography for detecting corneal ectasias. Combining AI with dual Scheimpflug and Placido imaging metrics offers a strong foundation for AI models in eye care.

Keywords:

Artificial Intelligence Deep Learning Imaging modalities Keratoconus Machine Learning Ophthalmology

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Published on: January 3, 2011

Area of Science:

Ophthalmology
Artificial Intelligence
Biomedical Engineering
Data Science

Background:

Interpreting corneal topographical maps for ectasia detection demands significant expertise.
Artificial intelligence (AI) offers potential solutions for automating corneal topography analysis.
Existing AI applications in this field vary in methodology and reported performance.

Purpose of the Study:

To review artificial intelligence (AI) algorithms applied to corneal topography.
To provide insights from the perspectives of an eye care professional, biomedical engineer, and data scientist.
To summarize advances in corneal imaging and AI applications for ectasia diagnosis.

Main Methods:

Systematic literature review conducted across Web of Science, Pubmed, and Google Scholar.
Literature search focused on AI in corneal topography from 2010 to 2020.
Analysis included imaging modalities, parameters, purpose, conclusions, samples, and AI performance.

Main Results:

Combined metrics from Dual Scheimpflug and Placido devices are identified as a promising starting point for AI models.
AI performance in keratoconus detection and classification showed high metrics: Area Under the Curve (0.87-1), sensitivity (0.89-1), and specificity (0.82-1).

Conclusions:

AI demonstrates significant potential in enhancing the accuracy and efficiency of corneal ectasia diagnosis.
A combination of different AI approaches is recommended for optimal diagnosis of corneal ectasias.
Integrated AI solutions leveraging multi-modal imaging data are crucial for future advancements.