# A Comprehensive Review of Artificial Intelligence Applications in Major Retinal Conditions

arXiv: https://arxiv.org/abs/2311.13710 · HTML: https://mlsanity.com/paper/2311.13710/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 501 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Contrastive Representation Distillation](https://mlsanity.com/paper/1910.10699/) (2019)
- [State-of-the-Art in Retinal Optical Coherence Tomography Image Analysis](https://mlsanity.com/paper/1411.0740/) (2014)
- [Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration](https://mlsanity.com/paper/2301.04525/) (2023)
- [Detecting Severity of Diabetic Retinopathy from Fundus Images: A Transformer Network-based Review](https://mlsanity.com/paper/2301.00973/) (2023)
- [Self-supervised Domain Adaptation for Breaking the Limits of Low-quality Fundus Image Quality Enhancement](https://mlsanity.com/paper/2301.06943/) (2023)

## Abstract (arXiv)

This paper provides a systematic survey of retinal diseases that cause visual impairments or blindness, emphasizing the importance of early detection for effective treatment. It covers both clinical and automated approaches for detecting retinal disease, focusing on studies from the past decade. The survey evaluates various algorithms for identifying structural abnormalities and diagnosing retinal diseases, and it identifies future research directions based on a critical analysis of existing literature. This comprehensive study, which reviews both clinical and automated detection methods using different modalities, appears to be unique in its scope. Additionally, the survey serves as a helpful guide for researchers interested in digital retinopathy.