# Artificial Intelligence in Dry Eye Disease

arXiv: https://arxiv.org/abs/2109.01658 · HTML: https://mlsanity.com/paper/2109.01658/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 104 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://mlsanity.com/paper/2011.03395/) (2020)
- [Evaluation Metrics for Unsupervised Learning Algorithms](https://mlsanity.com/paper/1905.05667/) (2019)
- [An automated and multi-parametric algorithm for objective analysis of meibography images](https://mlsanity.com/paper/2010.15352/) (2020)

## Built on by (most cited first, 1 of 1)

- [Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data](https://mlsanity.com/paper/2406.14068/) (2024)

## Abstract (arXiv)

Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term `AI' is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.