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Updated: Oct 11, 2025

Intense Pulsed Light for the Treatment of Dry Eye Owing to Meibomian Gland Dysfunction

Intense Pulsed Light for the Treatment of Dry Eye Owing to Meibomian Gland Dysfunction

Published on: April 1, 2019

Artificial intelligence in dry eye disease.

Andrea M Storås¹, Inga Strümke², Michael A Riegler²

¹SimulaMet, Oslo, Norway; Department of Computer Science, Oslo Metropolitan University, Norway.

The Ocular Surface

|November 29, 2021

Summary

This summary is machine-generated.

Artificial intelligence (AI) offers a path to more objective diagnosis of dry eye disease (DED). Machine learning techniques are being explored to improve DED diagnosis from medical images, though further development is needed.

Keywords:

Artificial intelligence Dry eye disease Machine learning

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Dry eye disease (DED) is highly prevalent but often underdiagnosed and undertreated.
Current diagnostic methods for DED can be subjective, relying on observer interpretation of images.
Artificial intelligence (AI), particularly machine learning, shows potential for objective medical diagnosis.

Purpose of the Study:

To review the current applications of AI in dry eye disease research.
To explore the potential of AI for improving DED diagnosis and management.
To provide an overview of AI techniques used in DED diagnostics.

Main Methods:

Literature review of studies utilizing AI in dry eye disease.
Analysis of AI applications in interpreting DED-related medical images.
Examination of machine learning models for DED diagnosis and severity stratification.

Main Results:

AI has been applied to interpret interferometry, slit-lamp, and meibography images in DED research.
Initial findings suggest AI can aid in classifying DED and predicting outcomes.
The review highlights the growing use of AI in various DED diagnostic tests.

Conclusions:

AI demonstrates promise for enhancing the objectivity and consistency of DED diagnosis.
Further research and clinical validation are essential for widespread AI adoption in DED care.
Standardization of AI models and rigorous clinical testing are crucial next steps.